Delimitation of ecological corridors in the Brazilian Atlantic Forest

Delimitation of ecological corridors in the Brazilian Atlantic Forest

Ecological Indicators 88 (2018) 414–424 Contents lists available at ScienceDirect Ecological Indicators journal homepage:

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Ecological Indicators 88 (2018) 414–424

Contents lists available at ScienceDirect

Ecological Indicators journal homepage:

Original Articles

Delimitation of ecological corridors in the Brazilian Atlantic Forest a


T a

Jeangelis Silva Santos , Catherine Cristina Claros Leite , Julyana Cristina Cândido Viana , ⁎ Alexandre Rosa dos Santosb, , Milton Marques Fernandesc, Vítor de Souza Abreua, Timóteo Paladino do Nascimentoa, Leandro Soares dos Santosa, Márcia Rodrigues de Moura Fernandesa, Gilson Fernandes da Silvaa, Adriano Ribeiro de Mendonçaa a b c

Federal University of Espírito Santo/UFES, PostGraduate Programme in Forest Sciences, Av. Governador Lindemberg, 316, 29550-000 Jerônimo Monteiro, ES, Brazil Federal University of Espírito Santo/UFES, Department of Rural Engineering, Alto Universitário, s/n, 29500-000 Alegre, ES, Brazil Federal University of Sergipe/UFS, Department of Forest Sciences, Av. Marechal Rondon, s/n, 49100-000 São Cristóvão, SE, Brazil



Keywords: Spatial analysis Multi-criteria analysis Forestry planning Brazilian Atlantic Forest

The Atlantic Forest is an intensely fragmented and globally important ecological hotspot. In this context, the objective of this study was to analyze the landscape ecology and submit proposals for creating ecological corridors (ECs) in a Brazilian Atlantic Forest region. Landscape ecology metrics, based on the forest fragments map, were used for the analysis. Suggested corridors were based on least-cost path analysis, considering land use, declivity, permanent preservation areas (PPAs), and forest fragment sizes. Although the predominant class sizes in the study area are small fragments, landscape ecology analysis has shown good environmental quality for fragments larger than 100 ha that do not lose their central area, even for the largest edge distances. Four ECs were proposed, with an average length of 53.86 km, average width of 5.39 km, and average area of 28,786.32 ha. Land use conflicts showed that the fragments within the corridors were situated in a matrix dominated by grassland. PPAs within the proposed corridors were dominated by misused land and did not comply with environmental legislation. The proposed corridors were efficient in using the largest fragments, which have the least edge effect and provide necessary support for most wildlife. However, we emphasize that other factors can influence the delimiting of ECs; additional studies are required to obtain more effective ECs to connect habitats. The proposed methodology can be applied to other Brazilian and global ecoregion.

1. Introduction The fragmentation of natural habitats is defined as a landscape-scale process involving forest loss, where the changing configuration or arrangement of forest cover (Fahrig, 2003; Long et al., 2010) is considered one of the main causes of biodiversity loss in habitats (BenítezMalvido et al., 2016; de Albuquerque and Rueda, 2010; Gibson et al., 2013; Kupfer et al., 2006; Rodríguez-Loinaz et al., 2012). Disordered land use and occupation, current economic models, and population growth (Ribeiro et al., 2009; Tabarelli et al., 2010; Tabarelli and Gascon, 2005) drive the fragmentation process and change the floristic and structural patterns of forest communities (Augusto et al., 2000; Carvalho, et al., 2016; Sousa et al., 2017). The barriers created by fragmentation include difficult dispersal between forest fragments,

reduced gene flow and genetic variability, and increasing risk of species extinction (Pelorosso et al., 2016; Tabarelli et al., 2010). The Brazilian Atlantic Forest is a globally important ecological hotspot (Araujo et al., 2015), considered one of the most important ecoregions in the world and is a priority for biodiversity conservation (Myers et al., 2000). This forest is home to about 5% of the world's flora and 2% of endemic vascular plants (Stehmann et al., 2009); 42.5% of Brazilian mammal species, of which 30% are endemic (Paglia et al., 2012); 75.6% of endangered species and endemic birds in Brazil (Marini and Garcia, 2005); and the second largest diversity of reptiles in Brazil (Sousa et al., 2010). However, the Atlantic Forest is also one of the most fragmented ecosystems and most explored Brazilian biome (Araujo et al., 2015), which, for centuries, has endured timber exploitation, agricultural development, farms, exotic tree plantations, and

Corresponding author. E-mail addresses: [email protected] (J.S. Santos), [email protected] (C.C.C. Leite), [email protected] (J.C.C. Viana), [email protected] (A.R. dos Santos), [email protected] (M.M. Fernandes), [email protected] (V. de Souza Abreu), [email protected] (T.P. do Nascimento), leandroefi[email protected] (L.S. dos Santos), [email protected] (M.R. de Moura Fernandes), [email protected] (G.F. da Silva), [email protected] (A.R. de Mendonça). Received 26 July 2017; Received in revised form 22 November 2017; Accepted 5 January 2018 1470-160X/ © 2018 Elsevier Ltd. All rights reserved.

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coordinates 41°47′11″ S, 20°26′46″ W. The Saíra Apunhalada covers an area 373.18 km2, located in southern Espírito Santo, centered around the coordinates 41°16′14″ S, 20°15′38″ W. According to the Köppen classification, the predominant climate in the region is of a subtropical altitude variety (Cwb), characterized by dry winters and mild summers (Oliveira et al., 2008). The study area is primarily in the Atlantic Forest, with formations of dense ombrophylous forest and seasonal semi-deciduous forest, and altitude fields in Parna Caparaó (Brasil, 2008). The region has an annual mean temperature of 18.9 °C and a mean precipitation of 1390 mm. The topography is characterized by fairly rugged relief, interspersed with reduced flat areas (Oliveira et al., 2008).

hunting (Laurance, 2009). This ecosystem originally covered about 150 million ha and is comprised of different types of vegetation as a consequence of the fragmentation process, maintaining only 12% of its original forest cover (Ribeiro et al., 2009). In this context, assessing the fragmentation and continuously monitoring forest areas are essential for understanding the characteristics of the landscape (Lele et al., 2008). Previous studies have analyzed the landscape ecology and structure in regions of the Atlantic Forest (da Silva et al., 2015; dos Santos Costa et al., 2016; Kauano et al., 2012; Pirovani et al., 2015; Saito et al., 2016). The Brazilian Government created and maintains Conservation Units (CUs) to protect and maintain the great remnants of natural ecosystems (Saito et al., 2016). However, CUs alone are insufficient to maintain the biodiversity of an ecosystem. (Beier and Noss, 1998). Conservation scholars have suggested regional networks of protected areas, interlinked with forest corridors, to prevent mass species loss (Lopes et al., 2009), also known as Ecological Corridors (ECs). ECs can be defined as vegetation strips linking forest fragments or CUs separated by human activity, promoting genetic flow between plant and animal communities and maintaining ecosystem functions and process (Beier and Noss, 1998; Harris and Gallagher, 1989; Harris and Scheck, 1991; Noss and Cooperrider, 1994; Noss, 1991). According to Perkl (2016), in the anthropogenic landscapes of the Atlantic Forest, connecting fragments using ECs is extremely important to increase conservation services. ECs are considered a viable solution both for connecting fragmented ecosystems and maintaining biodiversity (Seoane et al., 2010). Law n° 9.985/2000 (Brasil, 2000) defines ECs as “portions of natural or semi-natural ecosystems, linking CUs, which enable gene flow and biota movement, facilitating species dispersal and recolonization of degraded areas, as well as maintaining populations that demand for their survival areas with greater extension than that of the individual units”. Given the importance of ECs in the context of restoring fragmented landscapes and conserving protected areas, proposals to connect these areas are needed. In particular, proposing and deploying ECs along the landscape require, first at all, identify the most important aspects of the problem. One currently available technique is the least-cost path analysis (LCP), which is implemented in most Geographic Information Systems (GIS) (Driezen et al., 2007). The LCP analysis allows decisionmakers to determine the ideal way to connect two places within a cost surface. This can be accomplished by combining different criteria such as environmental impact and economic investment (Effat and Hassan, 2013). This modeling tool, derived from graph theory, is being increasingly applied to land and species management projects and research (Adriaensen et al., 2003; Effat and Hassan, 2013). The LCP has been used in conjunction with GIS in several studies to interconnect ecosystems mainly aimed at maintaining wildlife (Adriaensen et al., 2003; Carroll and Miquelle, 2006; Driezen et al., 2007; Ferrari et al., 2012; Hoctor et al., 2000; Larkin et al., 2004; LaRue and Nielsen, 2008; Li et al., 2010; Louzada et al., 2012; Rouget et al., 2006; Schadt et al., 2002; Walker and Craighead, 1997). In this context, the objective of this study is to analyze the landscape ecology and establish proposals to create ECs for a region in the Atlantic Forest in Brazil. In particular, we focus on interconnecting the Caparaó National Park, a priority area for conservation, using geotechnology.

2.2. Landscape ecology analysis The analysis of the dynamics of landscape ecology was conducted using the following methodological steps: 2.2.1. Step 1. Spatial database Images acquired by the Landsat-8 OLI satellite, all processing and analyses were performed in the software ArcGis® 10.3.1 (ESRI, 2015). The study area was delimited using as the vector files (shapefile) database for Parna Caparaó and Saíra Apunhalada, available from the Chico Mendes Institute for Biodiversity Conservation (ICMBio, 2016) and State Institute of Environment and Water Resources (IEMA, 2016). A 10 km buffer zone from the boundary of each area was applied, and then the files were edited to delimit the study area. 2.2.2. Step 2. Classification of land use and cover The land use and cover map of the study area was developed using the algorithm Segment Mean Shift for unsupervised classification, which identifies the segments in the image by grouping pixels with similar characteristics (ESRI, 2015). The image classified in raster format was converted to a shapefile (vector), and then the land use and cover map was trimmed (clipped) to the boundaries of the study area. 2.2.3. Step 3. Landscape ecology analysis The polygons of forest fragments were selected from the land use and cover map, and a shapefile file of the forest fragments was extracted. The fragments were classified according to their size in the following classes: a) very small (< 5 ha), b) small (5–10 ha), c) medium (10–100 ha), and d) large (> 100 ha). The forest landscape was analyzed using landscape ecology metrics in the software Fragstats®4.2 (McGarigal, 2013), based on the map of forest fragments in raster format. The metrics presented in Table 1 were selected to quantify landscape elements. The class area (CA) was calculated by size class, the area of all forest fragments of each class, corresponding the total area of the fragments present in the landscape, as well as the total edge (TE) is the sum of the lengths of all edge segments involving the corresponding patch type and edge density (ED) is the TE divided by the total landscape area. The number of patches (NP) measures the number of fragments for each type of land use and cover, indicating their fragmentation. The mean patch area (AREA_MN) is the calculation of the mean area of all fragments. Total core area (TCA) is the sum of all core areas, which are defined as the area within a fragment separated from the border by a predefined distance. Number of disjunct core areas (NDCA) is the sum of the number of disjunct core areas contained within each patch in the landscape. Mean core area (CORE_MN), standard deviation of core area (CORE_SD) and coefficient of variation of core area (CORE_CV) are the statistics mean, standard deviation and coefficient of variation calculated by core areas. Index area-weighted mean (CAI_AM) is the sum, across all patches in the landscape, of core area index (CAI) value multiplied by the sum of each patch area. CAI is used to calculate CAI_AM, being the patch core area divided by total patch area, in

2. Material and methods 2.1. Study area The study area covers the Caparaó National Park (Parna Caparaó) and an additional priority area for establishing ECs located approximately 60 km from Parna Caparaó, the Saíra Apunhalada (IEMA, 2006) (Fig. 1). The Parna Caparaó is located between the states of Minas Gerais and Espírito Santo, southeast Brazil, occupying an area of 317.61 km2 (ICMBio, 2016), centered around the geographical 415

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Fig. 1. Geographical location of the study area in southeastern Brazil.

2.3. Ecological corridors

Table 1 Metrics used to quantify the landscape structure in the study area. Metric

Acronym (unit)


Class Area Total Edge Edge Density Number of Patches Mean patch Area

CA (ha) TE (ha) ED (m.ha−1) NP (Dimensionless) AREA_MN (ha)

Area, Density, and Edge

Total Core Area Number of Disjunct Core Areas Mean Core Area Standard Deviation of Core Area Coefficient of Variation of Core Area Index Area-weighted Mean

TCA (ha) NDCA (Dimensionless) CORE_MN (ha) CORE_SD (ha)

Core Area

Proposals for implementing ECs interconnecting the Parna Caparaó and Saíra Apunhalada areas were made following methodological steps: 2.3.1. Step 1. Database The employed spatial database consisted of: a) study area limit, b) destiny polygon (Saíra Apunhalada), c) origin polygon (Parna Caparaó), d) Permanent Preservation Areas (PPAs), e) land use and cover, f) forest fragments, and g) Hydrological Consistency of Digital Elevation Model (HCDEM). To generate PPAs delimited according to the New Brazilian Forest Code (Law 12.727/12) and Conama Resolution 303/2002, a digital cartographic database was used. It was obtained from the Integrated System of Geo-referenced Bases of the State of Espírito Santo (GEOBASES), and included shapefile format files for hydrography and level contours. The following classes of PPAs were delimited, in accordance with Brazilian environmental legislation (Brasil, 2002, 2012):

CORE_CV (Percentage) CAI_AM (Percentage)

Source: McGarigal and Marks (1995).

percentage. The landscape ecology metrics were analyzed for each size class to compare the degree of conservation versus the mapped fragment size. To calculate the core area metrics of the fragments, we used distances of 20, 40, 60, 80, 100, 120, and 140 m of edge, obtaining different scenarios for analysis.

— Class 1 – Water courses (marginal strip): Obtained using the buffer tool, with a minimum range of 30 m from each margin because in the rainy season the width of the water courses does not exceed 10 m wide; — Class 2 – Springs: Obtained using the buffer tool with a radius of 416

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(ESRI, 2015). To trace the optimal paths, the function “Cost path” was used, which calculates the paths with lowest accumulated costs between the origin and destination, considering the previous maps. The LCP model is based on cumulative cost, which can be interpreted as the cost of moving through x cells, with different costs, that accumulate as they move away from the nearest source cell (Adriaensen et al., 2003; ESRI, 2015). After defining the least cost paths, the generated routes were individualized. Then, to determine the width of each corridor, a 10% buffer of the value of its respective length was applied according to Conama Resolution n° 09/1996 (Brasil, 1996). The length, width, and area were calculated for each EC.

50 m from the central point; — Class 3 – Slopes with slopes greater than 45° or 100%: were identified using the HCDEM, and a slope map was generated using the slope tool; — Class 4 – Altitudes higher than 1800 m, obtained using the reclassification function of the HCDEM; — Class 5 – Hill tops and mountains: Areas delimited from the level curve corresponding to two-thirds of the minimum elevation height relative to the base, according to the methodology proposed in Peluzio et al. (2010). The PPA classes were grouped in a single file in shapefile format, and discriminated with the five different classes. 2.3.2. Step 2. Spatial data edition At this step, “PPAs”, “land use and cover”, and “forest fragments” files for the study area (in vector format), were converted to raster format (function Polygon to raster). The declivity map, generated in the previous step, was reclassified (function Reclassify) for the following classes: a) 0–20°, b) 20–45°, and c) > 45°.

2.3.4. Step 4. Land use conflict in the ECs From the thematic maps of land use and cover and PPAs, a land use conflict analysis was conducted within each ecological corridor and their respective PPAs, evaluating the percentage of occurrence of each class.

2.3.3. Step 3. Least-cost path analysis To propose the best routes for implementing ECs, a LCP analysis was used to determine the path of least resistance between two points (origin and destination). The resistance of each cell is represented with weights, based on some factor, or combination of factors, that affect passage through the area. In this study, factors (parameters) that may influence the passage of EC in the landscape were selected as slope, land use and cover, PPAs, and fragment sizes. The definition of weights was adapted from Louzada et al. (2012), assigned on a scale of 1 to 100 according to the different classes in each parameter (Table 1 of Supplementary material). The lowest values were attributed to priority areas for EC, such as PPAs and forest fragments. From the assigned weights, cost matrix images for each parameter were generated. For each matrix image, the statistical weight was calculated using the hierarchical method Analytic Hierarchy Process (AHP), proposed by Saaty (1977). The AHP is a multi-criteria decision method to judge the relative weights of the different factors in the model (Li et al., 2010) (Table 2). Given the cost matrix images of each parameter and their respective statistical weights, we obtained the total cost matrix image, according to the equation below:

3.1. Landscape ecology analysis

3. Results

The distribution of forest fragments by size classes in the study area is presented in Fig. 2; 5,945 forest fragments were recorded, corresponding to an area of 124,346.04 ha of forest remnants. The class of very small fragments (< 5 ha) included the most fragments, 3,767, representing the largest percentage (63.36%) of the total number. Table 3 presents the landscape ecology metrics calculated in this study. The medium-sized fragments, due to higher TE, and small fragments, due to higher ED, are subject to a greater edge effect. The landscape ecology indices for the core area metrics are presented in Table 4. The highest TCA values were observed in the medium and large size classes. Higher values for the Number of Disjunct Core Areas (NDCAs) were found for smaller edge distances and in the very small size fragment class. The highest values of the CORE_MN metric were observed in the largest size classes (medium and large), which also presented a larger variation in core area based on observed values of CORE_SD and CORE_CV. The highest values of CAI_MN were observed in all size classes at the edge distance of 20 m. Only the large size class was able to maintain a CAI_MN greater than 50% for the longest edge distance.

TotalCost = 0.5806. LUseCost + 0.1141. PPAsCost + 0.0499. DecCost + 0.2554. FragCost

3.2. Proposals for implementing ecological corridors

where TotalCost is the total cost matrix image; LUseCost is the cost matrix image of land use and land cover; PPAsCost is the cost matrix image of PPAs; DecCost is the cost matrix image of declivity; and FragCost is the cost matrix image of forest fragments. After generating the matrix image of TotalCost , was used the function “Cost distance”, which generates distance and cost direction maps. The cost distance map represents the accumulation of costs as you move away from the nearest source. The cost direction map indicates the lowest cost path accumulated, from each cell, back to the nearest source

Four different routes of ECs, A, B, C, and D (Fig. 3), were generated using LCP analysis based on the total cost image. The ECs had an average length of 53.86 km, average width of 5.39 km, and average area of 28,786.32 ha. The conflict map for land use and cover in each proposed EC in Fig. 4. Based on the land use and cover conflicts for each EC (Table 5), corridor A has the largest forest cover area, and this class is considered a priority for conservation. Corridor A also had a larger area considered as barrier to EC passage, including the classes of rock outcrop or exposed soil, and agriculture, totaling 834.5 ha. In terms of percentages, the A corridor had the smallest area of barriers (2.4%) whereas corridors D and C had larger areas of barriers in relation to their size (2.67 and 2.56%, respectively). These classes were primarily considered barriers due to the higher costs of implementing ECs. The predominant class within the ECs was pasture, occupying an average area of 14,156.42 ha (49.42%). In percentage terms, corridor A presented a smaller pasture area. The PPAs have an average area of 11,153.09 ha, which represents 38.75% of the total average area of the ECs (Table 6). Corridors B, C,

Table 2 Paired comparison matrix and the statistical weights obtained using the AHP method.

Declivity PPAs Fragments Land use and cover




Land use and land cover

Statistical weight

1 3 5 9

1/3 1 3 5

1/5 1/3 1 3

1/9 1/5 1/3 1

0.0499 0.1141 0.2554 0.5806


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Fig. 2. Distribution of forest fragments in different size classes in the study area.

4. Discussion

Table 3 Landscape ecology indices calculated for the forest fragments in the region of Parna Caparaó and Saíra Apunhalada. Index


4.1. Landscape ecology analysis

Size classes Very small (< 5 ha)

Small (5–10 ha)

Medium (10–100 ha)

Large (> 100 ha)

3767 1.42 5356.80 6355.10 489

914 7.69 7030.98 13067.50 402

1171 37.56 43985.61 150126.40 376

93 731.37 68017.86 28682 320

The very small fragment size class (< 5 ha) had the highest number of fragments (NP). However, the largest size class obtained the lowest NP (Table 4), which verified that the landscape is very fragmented. Mitchell et al. (2015) pointed out that fragmentation, in addition to altering landscape structure, has an important effect on reducing the supply of ecosystem services. The study area had a very large NP for fragments < 5 ha and its sum in area was the smallest among all size classes analyzed. According to Moraes et al. (2015), forest species in isolated fragments may have their diversity reduced because they are subjected to edge effects due to fragment size and habitat loss. Thus, tropical landscapes are becoming a heterogeneous mosaic and small forest fragments lead to the simplification of animal communities and partial isolation of wild fauna (Ripperger et al., 2014). The AREA_MN of the very small and medium size class were low. We highlight the fragments of the large size class, which presented high value of AREA_MN. Medium class fragments presented the highest TE, which is related to high values of CA and NP. This means a larger area of contact with the surrounding matrix (Table 4). Surrounding matrix of the study area was immersed in a region with a predominance of coffee cultivation and pasture areas (Pirovani et al., 2015), which potentially increases edge effect. According to Moraes et al. (2015), the composition of the surrounding matrix should be taken into account regarding the

NP = Number of patches; AREA_MN = Mean patch area; CA = Class Area; TE = Total Edge; and ED = Edge Density.

and D had similar total areas of PPAs, while corridor A had a larger area (13,401.67 ha). The forest fragments class had the highest occurrence within the PPAs, with a mean of 6,341.18 ha (56.8%). Pasture was the second most representative class, occupying an average of 4,549.3 ha (40.87%), and was considered a misuse land within the PPAs. For land uses other than forest cover, rock outcrops and exposed soil (0.59%), agriculture (1.23%), planted forests (0.53%), and pasture (40.87%), corresponded to an average of 43.22% of the area occupied in the PPAs.


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smaller than 10 ha should be prioritized for measures to increase their area, such as reforestation or insulation of the area. The medium and large fragments classes showed a reduction in CORE_MN as the border distance increased, but did not present values close to zero (Table 5). The large size fragment class showed a large variation in edge distances for the CORE_MN metric, as indicated in the high CORE_SD and CORE_CV. This reflects that the class of large fragments should be subdivided into other classes (e.g. 100–200 ha, 200–300 ha, and > 300 ha) for CORE_MN analysis. The CAI_MN index showed larger values with larger fragment size. The very small and small size fragment classes tended to zero at an edge distance of 100 m. Pirovani et al. (2014) observed an edge distance of 100 m for the CAI_MN also tended toward zero in the same region of Serra do Caparaó and that this edge distance should be considered for environmental planning. Characteristics of the very small size fragments class should be accounted in for environmental planning for intervention in the landscape; there were many with small areas and were totally under the edge effect with a border distance of 100 m.

Table 4 Landscape ecology indices for the core area metrics. Edge distance (m)







Very small (< 5 ha) 20 5356.80 40 1806.57 60 380.97 80 148.68 100 15.21 120 3.96 140 1.35

3767.00 1745.00 861.00 437.00 35.00 8.00 2.00

1.42 0.48 0.10 0.04 0.00 0.00 0.00

1.93 0.90 0.35 0.21 0.08 0.04 0.02

136.04 188.59 341.21 531.70 1994.94 4275.53 5742.14

100.00 33.72 7.11 2.78 0.28 0.07 0.03

Small (5–10 ha) 20 7030.98 40 3790.98 60 1613.16 80 966.78 100 197.82 120 39.42 140 6.66

914.00 1138.00 1108.00 989.00 461.00 143.00 20.00

7.69 4.15 1.76 1.06 0.22 0.04 0.01

2.78 1.90 1.23 0.95 0.41 0.18 0.07

36.19 45.79 69.81 89.94 187.94 419.41 995.72

100.00 53.92 22.94 13.75 2.81 0.56 0.09

Medium (10–100 ha) 20 43985.61 40 32287.86 60 22917.60 80 19165.59 100 12570.30 120 9047.07 140 6388.56

1171.00 1796.00 1781.00 1805.00 1767.00 1709.00 1271.00

37.5624 27.5729 19.571 16.3669 10.7347 7.7259 5.4556

44.43 35.69 28.28 25.05 19.12 15.58 12.46

118.28 129.43 144.51 153.06 178.10 201.60 228.34

100.00 73.41 52.10 43.57 28.58 20.57 14.52

Large (> 100 ha) 20 68017.86 40 59809.68 60 52523.91 80 49261.32 100 42923.43 120 38892.60 140 35147.52

93.00 368.00 395.00 391.00 444.00 543.00 476.00

731.38 643.12 564.77 529.69 461.54 418.20 377.93

2741.71 2493.49 2267.30 2163.60 1959.86 1827.17 1702.27

374.87 387.72 401.45 408.46 424.63 436.91 450.42

100.00 87.93 77.22 72.42 63.11 57.18 51.67

4.2. Proposals for implementing ECs The proposed ECs to connect the Parna Caparaó and Saíra Apunhalada area, in the Brazilian Atlantic Forest of southern Espírito Santo, showed paths with lower costs of resistance along the surface, prioritizing the largest fragments. The size of the fragment and connectivity of the landscape are main structural features that influence the wealth and abundance of species (Antongiovanni and Metzger, 2005; Fischer and Lindenmayer, 2007; Martensen et al., 2008). Our study is unique in that we take into consideration the size of the fragments, giving priority to large fragments (> 100 ha). These fragments are more likely to have greater climatic variations, topography, and soil than the small fragments (Boecklen, 1986), favoring the establishment of species. Larger fragments usually contain more species and larger populations, which theoretically increases stability against variations in demographics, environmental change, and genetic processes (Metzger, 2009). Furthermore, larger fragments are less susceptible to edge effects, and hence have a larger core area, which is extremely important, especially for species with high area demands. Previous research has shown the positive influence fragment size has on variations in abundance and faunal diversity in the Atlantic Forest, in species of birds (Maldonado-Coelho and Marini, 2004; Martensen et al., 2008; Uezu et al., 2005), mammals (Pardini et al., 2010, 2005), insects (Araujo et al., 2015), amphibians (Almeida-Gomes et al., 2016; Dixo et al., 2009), and reptiles (Almeida-Gomes and Rocha, 2014; Cabrera-Guzmán and Reynoso, 2012). The selection of larger areas for biodiversity conservation is suggested as a “golden rule,” primarily because of its ability to maintain larger populations and better prospects for sustaining species over the long term (Brooks et al., 1999). In less connected landscapes, where species loss with reduction in fragment area is intensified, fragment size is of greater importance (Marini, 2001). However, in scenarios where small fragments are located close to large fragments, connectivity is as important as or even more important than size (Martensen et al., 2008; Metzger, 2000). The connectivity allows species to explore the landscape in different ways, enabling the maintenance of a large number of species, even in small fragments, because individuals can use different fragments nearby, maintaining marginal populations for rescue purposes or recolonization (Martensen et al., 2008). Therefore, just as large fragments are essential, the small fragments are important in reducing the isolation of fragments in the landscape. For species with good abilities to move through the matrix, small fragments act as trampolines for dispersion between fragments, being more important the connectivity than the fragment size (Saura and Rubio, 2010).

TCA = Total core area; NDCA = Number of Disjunct Core Areas; CORE_MN = Mean Core Area; CORE_SD = Standard Deviation of Core Area; CORE_CV = Coefficient of Variation of Core Area; CAI_AM = Index area-weighted mean.

connectivity of fragments for displaced wild fauna. The fragmentation of the landscape restricts the movement of animals between habitats, also limiting the faunal ability to explore and select the optimal habitats, promoting competition for low quality patches (van Langevelde, 2015). The generally small fragment size resulted in higher ED in the study region (Table 4). According to Juvanhol et al. (2011), ED is inversely proportional to the size of the fragment. Therefore, the smaller fragments are more susceptible to the edge effect, thus underscoring the importance of establishing restoration actions to minimize the edge impact in these fragments. The highest values of TCA were observed in the large size class. Simulations with different edge distances indicated that the very small and small size classes obtained the lowest values of TCA (Table 5). One way to increase the TCA of the very small and small fragments would be the implantation of a buffer area around it. The Number of Disjunct Core Areas (NDCA) had lower values in the two smaller size classes (very small and small fragments), and was also impacted by increased edge distance (Table 5). Pirovani et al. (2015) observed that decreasing fragment area due to the edge effect facilitated a reduction in NDCA. This compromises the quality of the fragments in the landscape because it reduces the number of habitat areas without alterations within the fragments. Therefore, the smallest fragments are fully under the edge effect. The smaller (very small and small) fragments presented Mean Core Area (CORE_MN) tending toward zero in the sensitivity analysis of how varying edge distance influences metric results. These results corroborate the other core area metrics, which demonstrate that very small and small fragments lose the core area due to the edge effect. Fragments 419

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Fig. 3. Corridors A, B, C, and D generated for connecting Parna Caparaó and the Saíra Apunhalada area, Espírito Santo, Brazil.

incorrect choice of fodder species, initial malformation, lack of maintenance fertilization, and inadequate pasture management (Peron and Evangelista, 2004). Inappropriate management practices, such as overgrazing, soil compaction, and exposed soil areas (Pirovani et al.,

Here, land use conflicts showed that fragments along the corridors were embedded in a pasture-dominated matrix interspersed with agricultural crops. It should be emphasized that the surrounding pasture is in the degradation stage, caused by several factors, among them, the 420

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Fig. 4. Conflict map for land use in each proposed EC for the study area.

Table 5 Comparison of land use and cover for each EC corridor proposed between the Parna Caparaó and Saíra Apunhalada area, Espírito Santo, Brazil. Class

Rocky outcrop and bare soil Agriculture Forest fragments Planted forests Pasture Total area

Corridor A

Corridor B

Corridor C

Corridor D









77.29 757.21 17,705.22 180.66 16,062.73 34,783.11

0.22 2.18 50.90 0.52 46.18 100.00

167.70 491.28 12,962.22 103.84 13,227.02 26,952.06

0.62 1.82 48.09 0.39 49.08 100.00

320.75 331.31 11,561.10 69.39 13,212.85 25,495.40

1.26 1.30 45.35 0.27 51.82 100.00

407.66 338.39 12,976.23 69.33 14,123.07 27,914.69

1.46 1.21 46.49 0.25 50.59 100.00


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Table 6 Land use and cover conflicts in PPAs in each EC proposed between the Parna Caparaó and Saíra Apunhalada area, Espírito Santo, Brazil. Class

Rocky outcrop and bare soil Agriculture Forest fragments Planted forests Pasture Total area of PPAs % in relation to the EC area

Corridor A

Corridor B

Corridor C

Corridor D









28.05 215.77 7,708.16 92.17 5,357.53 13,401.67 38.53

0.21 1.61 57.52 0.69 39.98 100

48.14 147.48 5,964.68 59.79 4,271.21 10,491.31 38.93

0.46 1.41 56.85 0.57 40.71 100

73.36 95.34 5,418.38 44.84 4,094.00 9,725.93 38.15

0.75 0.98 55.71 0.46 42.09 100

101.16 98.77 6,273.51 45.57 4,474.46 10,993.46 39.38

0.92 0.90 57.07 0.41 40.70 100.00

2015), compromise the flow of wildlife. The configuration of the surrounding matrix of the habitat can affect the processes that take place inside the fragments (Kupfer et al., 2006). The results found in this study were primarily due to the historical processes incident on the different regions of the Atlantic Forest, which has long suffered from poor land use management and extensive destruction of forests (Ribeiro et al., 2009). The remaining fragments of this important ecosystem are structurally isolated within a matrix of pastures, plantations, or urban areas, with the majority of these remnants being small (Vieira et al., 2009). Public policies, however, do not encourage the management of isolated remnants, of which 75% are privately owned (Brannstrom, 2001). The surrounding region of the Parna Caparaó and Saíra Apunhalada area is mostly formed from very small fragments (< 5 ha). Thus, we suggest that some appropriate land use management measures should be adopted to protect these remnants; in particular, because they are more susceptible to anthropic action. Once the best routes ECs have been defined, public policies aimed at incentives, such as financial remuneration or tax reductions, should be implemented for owners of the remnant forest areas. The results presented for conflict land use in PPAs show that most conflicts were due to land misuse, evidencing non-compliance with current environmental legislation (Forest Code). Other studies analyzing conflict zones in PPAs in the region corroborate with these results (Luppi et al., 2015; Moreira et al., 2015; Zanella, 2011). The Brazilian environmental legislation, through the New Forest Code, requires owners to maintain a proportion of forest on their properties, often requiring restoration of degraded habitat (Costa et al., 2016). Another alternative for recovering degraded pastures is planting agroforestry systems (AFS), combining native species with exotic or fruitful, as permitted and encouraged by law (Brasil, 2012, 2013). Reconstituting PPAs must be achieved with the natural regeneration of native species or planting of native species, which may be intercalated with exotic species (Brasil, 2012). Rural extension programs should be adopted, focusing on integrated management and conservation of natural resources, soils, water, and biodiversity, thus enhancing the environmental services provided by LR and PPA areas, as required by the Forest Code (Okuyama et al., 2012). Other preventive measures should be adopted, such as more effective monitoring of environmental agencies and public management policies aimed at the recovery and preservation of these areas, especially PPAs. In the current extremely fragmented and dynamic Atlantic Forest, the importance of the remaining large forest remnants increases exponentially (Ribeiro et al., 2009). However, conservation management actions and landscape recovery programs should not only focus on large fragments (Martensen et al., 2008). The conservation of small fragments should not be neglected, as they constitute a large part of the remnants, and are essential to strengthening connectivity between the larger fragments (Ribeiro et al., 2009). In this study, LCP modeling was used to propose corridors based on land use classes, declivity, PPAs, and fragment size. The methodology allowed us to assign greater importance to higher priority variables for

corridor passages. In practice, this is highly relevant to researchers, land managers, and landowners. The LCP technique is useful for land managers because the surface can be parameterized based on the best available data. Thus, the surface can be adapted to the characteristics of the landscape for which the manager has knowledge and experience (Bunn et al., 2000). On the other hand, the disadvantages of this method are that it assumes that animals possess a perfect knowledge of the landscape and seek to move for specific purposes. However, animals may have their preferred habitats and choose displacement routes based on other preferences. Therefore, the data obtained through GIS may not faithfully reproduce the factors used by organisms to decide their movement in fragmented habitats. We emphasize that within the proposed ECs there was a predominance of the pasture class, which necessitates adopting land use management measures that facilitate corridor function. Most PPAs are affected by land misuse. Although the forest cover class shows the greatest participation in PPAs, which is a positive result in terms of recovery and application of environmental legislation, the land misuse classes are well represented. When analyzing the most appropriate conditions and barriers in each proposed corridor, it is noted that the greatest potential for EC implementation was found with proposed corridor A. Notably, this corridor, in addition to having larger areas suitable for passage of ECs (forest cover), has a considerably higher area than the other ECs. Therefore, implementation of this corridor would be more expensive, due to the need for intervention in larger areas. The maintenance and restoration of corridors are strategies that improve the probability of persistence of animal species in small patches of tropical landscapes that have already suffered high levels of deforestation, as is in the Atlantic Forest (Pardini et al., 2005). A new paradigm for the conservation of this ecosystem is urgently needed; the regional planning approach should incorporate the protection of landscapes linked by original or restored vegetation corridors representing several thousand hectares of forest (da Silva and Tabarelli, 2000).

5. Conclusions The studied landscape is of good environmental quality considering that the large fragments do not lose their core area, even with largest border distances. However, most of the fragments were very small, and these were more affected by the edge effect. Therefore, these fragments should also be considered in environmental planning for interventions in the landscape. This study presented efficient proposals for implementing ECs, considering slope, land use, PPAs, and fragment size. The prioritization of large fragments is an interesting strategy from an ecological point perspective, because they present greater potential for conservation of biodiversity. However, we emphasize that other factors can influence the delimiting ECs; additional research is required to obtain more effective ECs to connect habitats. The pasture class dominates the landscape of the study area, making 422

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it necessary to adopt land use management measures that facilitate EC function. The methodology proposed for the implementation of ECs can be applied to other Brazilian biomes as well as biomes in other countries.

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