Open Access

Impact of deforestation on habitat connectivity thresholds for large carnivores in tropical forests

  • Miriam A. Zemanova1, 2,
  • Humberto L. Perotto-Baldivieso3, 4Email author,
  • Emily L. Dickins5,
  • Andrew B. Gill1, 6,
  • John P. Leonard3 and
  • David B. Wester3
Ecological Processes20176:21

DOI: 10.1186/s13717-017-0089-1

Received: 22 February 2017

Accepted: 23 May 2017

Published: 13 July 2017

Abstract

Introduction

Deforestation significantly impacts large carnivores that depend on large tracts of interconnected forest habitat and that are sensitive to human activities. Understanding the relationship between habitat use and spatial distribution of such species across human modified landscapes is critical when planning effective conservation strategies. This study assessed the presence of potential landscape connectivity thresholds resulting from habitat fragmentation associated with different deforestation patterns using a scale-based approach that links species-specific home ranges with the extent of anthropogenic activities. The objectives were (1) to quantify the spatial and temporal distribution of natural vegetation for five common deforestation patterns and (2) to evaluate the connectivity associated with these patterns and the existence of potential thresholds affecting jaguar dispersal. The Bolivian lowlands, located within jaguar conservation units, were analysed with landscape metrics to capture the spatial and temporal changes within deforested areas and to determine potential impact on jaguar connectivity and connectivity thresholds for dispersal.

Results

Over the period of 1976–2005, the amount of natural vegetation has decreased by more than 40% in all locations with the biggest changes occurring between 1991 and 2000. Landscape spatial structure around jaguar locations showed that jaguars used areas with mean proportion of natural areas = 83.14% (SE = 3.72%), mean patch density = 1.16 patches/100 ha (SE = 0.28 patches/100 ha), mean patch area = 616.95 ha (SE = 172.89 ha) and mean edge density = 705.27 m/ha (SE = 182.19 m/ha).We observed strong fragmentation processes in all study locations, which has resulted in the connectivity of jaguar habitat decreasing to <20% by 2005. A connectivity threshold zone was observed when the proportion of natural vegetation was less than 58.4% (SE = 1.3).

Conclusions

Assessing fragmentation and connectivity for carnivores within the extent of human-modified landscapes proved to be an effective way to understand the changes caused by deforestation and their potential effects on large carnivore habitats. Our study highlights the importance of scale-based approaches for assessing current conservation challenges to protect large carnivores.

Keywords

Bolivian lowlands Habitat fragmentation Landscape metrics Panthera onca Threshold

Introduction

Pressures, such as population growth and socio-economic improvements to human welfare, have significantly increased demands for agricultural goods and services (Rudel and Roper 1997; Lorena and Lambin 2009; Ziolkowska et al. 2012). Increased deforestation rates across the world in particular have had profound consequences for species composition and distribution (Zipperer 1993). The impacts of deforestation have been widely documented through changes in the spatial configuration and habitat quality of remaining habitat fragments, biophysical functions of the landscape, fauna and flora composition and loss of natural habitat (e. g. Steininger et al. 2001a; Steininger et al. 2001b; Dixo et al. 2009; Scolozzi and Geneletti 2012). One of the consequences of deforestation and a major challenge in conservation biology is habitat fragmentation (Zipperer 1993; Dixo et al. 2009; Rueda et al. 2013). In recent years, it has become increasingly important to better understand the ecological consequences of fragmentation due to the expansion and intensification of land use (Baggio et al. 2011; Ziolkowska et al. 2012).

Fragmentation by deforestation occurs through the loss of natural habitat and the pattern of clearing, which result from the interactions of biotic, abiotic and human socio-political factors (Zeilhofer et al. 2014). Fragmentation has a significant effect on overall habitat availability and quality (Dixo et al. 2009) with consequences for species survival, abundance, dispersal and the functional linkages amongst habitat patches (Scolozzi and Geneletti 2012; Gao et al. 2013). Different fragmentation patterns (i.e. geometric, corridor, fishbone, diffuse, radial and island) caused by deforestation have contributed to different species survival and dispersal rates (Geist and Lambin 2001; Millington et al. 2003; Lorena and Lambin 2009) and can limit species’ ability to colonise an area by imposing an isolation barrier (Kindlmann and Burel 2008). Geist and Lambin (2001) and Lorena and Lambin (2009) provide detailed descriptions of abovementioned deforestation patterns. These landscape changes are particularly important for large carnivores because they depend on large tracts of undisturbed habitat and are highly sensitive to human activities, such as habitat loss and degradation, prey depletion by local communities hunting and perceived threats to those communities and their domestic species (Jackson et al. 2005; Cavalcanti and Gese 2009, 2010; Horev et al. 2012; Zeilhofer et al. 2014). Fragmentation also affects the gene flow within declining populations and hence increases extinction risk (Vandermeer and Lin 2008; Zeilhofer et al. 2014; Roques et al. 2016). Therefore, understanding the loss of connectivity that can affect the dispersal ability and movement patterns of large carnivores across fragmented landscapes is critical for implementing conservation strategies (Pascual-Hortal and Saura 2006; Vogt et al. 2009).

Landscape connectivity, defined as the degree to which a landscape facilitates or impedes movement of a species (Taylor et al. 1993), is critical because of its direct impact on the maintenance of ecological function and population persistence and survival of a species (Pascual-Hortal and Saura 2006; Kool et al. 2013). The use of graph theory has proven valuable in addressing structural connectivity measures across different scales and has been shown to be a robust methodology to assess patch and landscape connectivity (Saura and Pascual-Hortal 2007; Scolozzi and Geneletti 2012). Quantifying structural connectivity is species-specific and requires relevant behaviour and dispersal information to define spatial and temporal scales for connectivity assessment (Dixo et al. 2009; Perotto-Baldivieso et al. 2009; Kool et al. 2013). Addressing fragmentation and structural connectivity by integrating the scales at which deforestation processes and wildlife dispersal operate can significantly contribute to a new conservation paradigm (i.e. incorporation of human modified landscapes in conservation strategies for species dispersal) proposed by Chazdon et al. (2009). Furthermore, scale-based approaches can represent a solution to scale mismatches that have resulted in failure to achieve conservation objectives (Delsink et al. 2013).

Assessing species dispersal within the extent of human activities that cause landscape fragmentation can provide an indication of critical thresholds at which a species can be significantly impacted by fragmentation (Oliveira de Filho and Metzger 2006). Whilst there is evidence that these thresholds are important to identify breakpoints where species dispersal significantly changes owing to loss of habitat, there is growing consensus that there is not a single ‘fragmentation threshold’ applicable across species because of significant variability in the response of wildlife to habitat structural changes (Swift and Hannon 2010; Martensen et al. 2012). Specific ecological thresholds should be identified for individual species (Johnson 2013). To date, it has been difficult to integrate processes operating at different scales, such as deforestation and species movement, to successfully achieve conservation objectives (Delsink et al. 2013). It is critical to determine whether there are minimum amounts of natural vegetation that may limit species dispersal across these modified landscape patterns.

The tropical lowlands of Bolivia have been affected considerably by anthropogenic activities in the last three decades (Killeen et al. 2007). Agricultural subsistence colonisation (1970s), spontaneous colonisation (1980s) and industrial agriculture initiatives (1990s) have significantly increased deforestation in the country (Steininger et al. 2001b; Millington et al. 2003; Killeen et al. 2007; Killeen et al. 2008; Arancibia Arce et al. 2013). These events have generated distinctive deforestation patterns (e.g. geometric, diffuse, fishbone, radial) across areas (Geist and Lambin 2001; Steininger et al. 2001a; Mertens et al. 2004) with significant impacts on natural vegetation. These areas have also been identified as critical for the dispersal of jaguars (Panthera onca) in their southern distribution range (Sanderson et al. 2002; Rabinowitz and Zeller 2010). The jaguar is an appropriate choice for this study because of its broad but declining distribution and its value as a flagship species across the Americas (Sanderson et al. 2002; Cavalcanti and Gese 2010; Rabinowitz and Zeller 2010; Tobler et al. 2013). Jaguars are an excellent example of a large ranging predator that depends on undisturbed habitat for movement across a variety of ecosystems (Baggio et al. 2011). They are also very sensitive to human presence and human activities (De Angelo et al. 2013). Where human activities focus on livestock production, jaguars could shift their behaviour towards prey preferences from wildlife species to domestic livestock (Cavalcanti and Gese 2009, 2010). On the other hand, where human activities focus on farming, there is a potential for natural prey loss and habitat degradation (Zeilhofer et al. 2014). In both cases, habitat loss and potential persecution by local farmers and ranchers significantly affect population persistence (Petracca et al. 2014).

Therefore, the aim of this study was to assess the presence of potential structural connectivity thresholds due to fragmentation in different deforestation patterns with a scale-based approach that links species-specific home ranges with the extent of anthropogenic activities. The objectives were (1) to quantify the spatial and temporal distribution of natural vegetation for five common deforestation patterns over three decades and (2) to evaluate the degree of connectivity and the existence of potential thresholds that could affect jaguar dispersal. We hypothesised that the deforestation patterns identified in our study area would be reflected in significantly different habitat fragmentation and structural connectivity for jaguars until a threshold zone between the proportion of natural vegetation and connectivity is reached.

Methods

Study area

The study site was located in the Bolivian lowlands and foothills East of the Andes (Fig. 1a) where large-scale deforestation driven by agricultural activities has taken place in the last three decades (Fig. 1c–g) (Killeen et al. 2007, 2008). This area is located at the centre of areas identified with good potential for jaguar conservation (known as jaguar conservation unit (JCU)) (Fig. 1a) proposed initially by Sanderson et al. (2002) and further developed by Rabinowitz and Zeller (2010). The five study locations (SA) were selected according to the deforestation pattern and socio-economic situation identified in the Bolivian lowlands (Fig. 1b). Deforestation patterns were described using the typology of Geist and Lambin (2001):
Fig. 1

Location of study area in Bolivia and within national protected areas and jaguar conservation units (Sanderson et al 2002, Rabinowitz and Zeller 2010) (a). Jaguar observations between 1986 and 2005 and study area with sampled location (b): Chapare region (SA1), northwest colonization zone (SA2), Santa Cruz integrated zone (SA3), southern expansion zone (SA4) and northern expansion zone (SA5). Deforestation are shown in dark grey in the study area for 1976 (c), 1986 (d), 1991 (e), 2001 (f) and 2005 (g)

  • The Chapare region (SA1; pre-Andean and lowland Amazon forests, most humid) is driven mainly by small scale subsistence and commercial agriculture, coca (Erythroxylum coca) production and timber harvesting (Millington et al. 2003; Bradley and Millington 2008). Crops are mostly perennials grown extensively (Superintendencia Agraria 2001). Deforestation has been undertaken in fishbone patterns expanding out from main and secondary roads. The Chapare region is recognised for its importance to biodiversity, and it is surrounded by three National Parks in the area: Carrasco, Amboró and Isiboro Sécure.

  • The northwestern colonization zone (SA2; lowland humid and semi-deciduous, Amazon and Chiquitano forests) is one of the oldest and most successful small-scale farming schemes (Mertens et al. 2004). This area was colonized by directed migrations, causing a diffuse pattern initially which later coalesced into a more geometric pattern of intensified deforestation, with most deforestation (~80%) occurring before 1995. Currently most of the land use conflicts occur between timber companies, forest reserves (el Chore forest reserve) and spontaneous migrations into the area.

  • The Santa Cruz integrated zone (SA3; lowland Chiquitano semi-deciduous forest) is inhabited by Cruceño farmers (Cruceño refers to the people from Santa Cruz Department), Japanese and Mennonite farmers. This area is known to be the traditional centre of large-scale agriculture in the Bolivian lowlands (Mertens et al. 2004). This region was one of the first areas to be subject to large-scale deforestation with geometric patterns mostly occurring before 1980. Crops in this area are grown on rotation combined with improved and natural pastures. This area has one of the most developed road infrastructures in the lowlands with a very high road density.

  • The southern expansion zone (SA4; lowland Chiquitano and Chaco dry forests) lies east of the Rio Grande River. The mechanised farming sector has grown steadily (Vanclay et al. 1999), managed mainly by large agro-industrial corporations. Intensive rotational cropping systems dominate the agricultural activities in the area. This has resulted in large-scale farmers clearing very large blocks of land, mostly eastwards of the Santa Cruz integrated zone (SA3) following large geometric patterns. The national protected area, Kaa-Iya del Gran Chaco National Park lies to the southeast of this area.

  • The northern expansion zone (SA5; lowland humid Chiquitano forest) originated from the San Julián resettlement scheme of indigenous people from the Andes where small nucleated settlements lie central to soybean agricultural fields extending out in radial patterns (Steininger et al. 2001a). Crops are grown in extensive rotations. Classic fishbone patterns along the secondary roads, combined with island deforestation patterns from small settlements, are removing natural vegetation composed of deciduous and semi-deciduous forests (Mertens et al. 2004).

Data collection and analyses

To facilitate the understanding of deforestation patterns and fragmentation that affect jaguars across a variety of habitats, we focused on the natural and anthropogenic changes between 1976 and 2005. The land-cover change database published by Killeen et al. (2007) was reclassified in ArcGIS (ESRI; Redlands, CA) into three categories: natural, anthropogenic and water. This database represents the land-cover in the study area in years 1976 (Fig. 1c), 1986 (Fig. 1d), 1991 (Fig. 1e), 2001 (Fig. 1f) and 2005 (Fig. 1g). The details on the original land cover classes, satellite platform (LANDSAT program), spatial resolution (30 m) and the classification methodology for the land-cover change database are described in Killeen et al. (2007). Two scales were used for our analyses: the scale or extent of human activity and within this scale, the extent of female jaguar home ranges reported by Maffei et al. (2004). For each study location (SA1–SA5), spatial sampling captured the extent of the deforestation process caused by human activities and its variability within study locations. Using the 2005 land-cover database map (Killeen et al. 2007), evenly separated rectangular blocks were delineated perpendicular to the main road associated with deforestation. The length was defined by the extent of continuous deforestation for each SA (Fig. 1b). Therefore, the size of each sampling block was different for each location (Table 1), but each sampling block was separated by 10 km to reduce spatial and/or temporal autocorrelation (Odland 1988; Minta 1992). Spatial autocorrelation refers to the degree of similarity between observations recorded at points that are close to each other in space and/or time. Minimizing autocorrelation provides independent data for valid statistical analyses (Perotto-Baldivieso et al. 2012a). The 10 km distance was chosen because it represents at least three times the mean diameter for jaguar home ranges reported by Maffei et al. (2004). For each block and for each time period, fragmentation was quantified with the landscape metrics: percentage of natural vegetation cover (%; PLAND), mean patch size (ha), patch density (patches/100 ha) and edge density (m/ha) (Millington et al. 2003; Perotto-Baldivieso et al. 2011; McGarigal et al. 2012; Zeilhofer et al. 2014). Mean and standard errors for each metric were calculated for each location and time period between 1976 and 2005.
Table 1

Sampling design for the five deforestation patterns

Study area

SA1

SA2

SA3

SA4

SA5

Number of transects

5

6

4

4

4

Dimensions (km)

60 × 20

30 × 10

40 × 15

100 × 20

40 × 10

Area (km2)

1200

300

600

2000

400

Spacing (km)

15

10

15

15

10

Connectivity was assessed for each sampling block and time period using the integral index of connectivity (IIC) (Pascual-Hortal and Saura 2006). This metric is a graph-based index employed to assess connectivity using graph theory (Neel 2008; Perotto-Baldivieso et al. 2009; Visconti and Elkin 2009) and it was calculated using Conefor Sensinode 2.6 (Saura and Torne 2009). Connectivity values can range from zero to one, with one being the highest connectivity value. To understand the impact of fragmentation on jaguar habitat connectivity within each block, a distance threshold of 3 km was used between connecting patches. This distance corresponds to the radius of an average home range of 29 km2 reported for female jaguars in continuous dry forest habitats in the eastern regions of Bolivia (Maffei et al. 2004), the closest home range observation in the region.

The relationship between PLAND and IIC was analysed with linear and nonlinear regression models using the results from the sampling blocks and time periods (Table 1). A straight-line relationship between connectivity and natural vegetation would indicate that the estimated change in connectivity with a unit change in natural vegetation is constant across the range of natural vegetation, which in turn suggests that there is no breakpoint in natural vegetation with respect to corresponding changes in connectivity (Gujarati and Porter 2009). In contrast, a curvilinear relationship between connectivity and natural vegetation, which can be estimated with a number of models (e.g. multiple linear polynomial models, simple nonlinear exponential model) would suggest the presence of a breakpoint (or region) where connectivity changes in a different way to changes in natural vegetation which can be useful for applications in conservation and management strategies. Because we lacked replicated values of connectivity for given values of natural vegetation, a formal test of lack-of-fit of a simple linear model (Kutner et al. 2004) was not possible. However, presence of a significant quadratic term in a polynomial model indicates that a curvilinear relationship is a better fit than a simple linear model (Kendall and Stuart 1966). We used PROC REG in SAS 9.3 (SAS Institute INC.; Cary, NC) to estimate connectivity as a function of natural vegetation in simple linear and quadratic polynomial models in PLAND; PROC NLIN in SAS 9.3 (SAS Institute INC.; Cary, NC) was used for a simple nonlinear exponential model. Because both polynomial and exponential models were a better fit than a simple linear model, we concluded that there was a curvilinear relationship between connectivity and natural vegetation, and so, we estimated a breakpoint using Davies test (Davies 1987; Muggeo 2003; Muggeo 2008) and piecewise linear regression methods (Gujarati and Porter 2009) both of which provide standard errors of estimated breakpoints. We used the estimated breakpoint from Davies test as the starting point in PROC NLIN for the parameter search for C in the model l.
$$ \mathrm{I}\mathrm{I}\mathrm{C} = {\beta}_0+{\beta}_1\mathrm{PLAND} + {\beta}_{2\ }\left(\mathrm{PLAND}- C\right) D + {\epsilon}_i $$
(1)

where C is estimated breakpoint and D is dummy variable equal to 0 when PLAND < C and 1 when PLAND ≥ C. Analysing Eq. 1 with a multiple linear regression analysis (PROC REG, SAS 9.3, Cary, NC) with the independent variable (PLAND–C) yielded similar results for breakpoint values in the piecewise linear regression method and Davies test.

Additionally, available jaguar observation data (capture data, sightings, faecal samples, footprints) collected between 1985 and 2007 located in the study area were acquired from Lopez-Strauss et al. (2010) and downloaded from the Geospatial Centre for Biodiversity (www.museonoelkempff.org/cgb; Perotto-Baldivieso et al. 2012b) to quantify the landscape spatial structure around areas where these observation data were recorded. A total of 777 jaguar observation records (680 records from Lopez-Strauss et al. (2010), and 97 records from Geospatial Centre for Biodiversity) were publicly available for the entire country. From this database, only 63 records were located within our study area and 39 records (one record 1986, one record 1994, one record 1995, 14 records 1996, six records 1997, one record 1999, four records 2000, one record 2001, two records 2002, two records 2004, five records 2005, one record 2007) were used. The remaining 24 observation records had to be excluded due to the lack of information regarding the year of observation (15 records) or potential record redundancy (three records had same coordinates and same year in three different locations: nine records total). The purpose of this analysis was to quantify the spatial structure of natural vegetation that was potentially used by jaguars based on published and verified observations within the study area. For each of the used jaguar observations, a circular buffer area representing a 29 km2 home range was simulated and the same landscape metrics from our spatial modelling (i.e. PLAND, mean patch size, patch density and edge density) were calculated using FRAGSTATS 4.2 (McGarigal et al. 2012). Mean values and standard errors were calculated for these metrics.

Results

Over the period of 1976–2005, PLAND decreased in all locations with the biggest changes occurring between 1991 and 2000. The areas most affected by these changes were the expansion zones SA4 (geometric deforestation patterns) and SA5 (radial deforestation patterns) where the percentage of natural vegetation decreased from 71 to 79%, respectively, in the 1990s to 37% in 2001 (SA4 and SA5) (Fig. 2a). The high variability in the proportion of natural vegetation (Fig. 2a) indicates that deforestation processes were occurring at different rates within locations. Patch density increased between 1976 and 2005 in all locations (Fig. 2b), with reduction in mean patch area (Fig. 2c) and increase in edge density (Fig. 2d). Hence, fragmentation process occurred in all locations, with the highest rates observed in the expansion zones SA4 and SA5 followed by the newly colonised zones SA1 (fishbone deforestation pattern) and SA2 (diffuse deforestation pattern). Patch density (Fig. 2b) and edge density (Fig. 2d) values increased throughout the study period in SA1, SA2 and SA5, whereas peak values were observed in 1991 for SA3 (geometric deforestation patterns) and SA4. Mean nearest neighbour distances increased (Fig. 2e), and IIC decreased in all locations with values at their lowest (<0.2) after 2001 (Fig. 2f). Lowest IIC values were observed in SA2 and SA3. The highest declines in IIC were observed in the expansion zones SA4 and SA5 where agriculture increased significantly after the 1990s.
Fig. 2

Landscape metrics describing fragmentation and connectivity in five deforestation patterns in the lowland tropical forests in Bolivia: proportion of natural vegetation (a), patch density (b), mean patch area (c), edge density (d), mean nearest neighbour distance (e) and connectivity (f). The five deforestation patterns are the Chapare region (SA1), northwest colonization zone (SA2), the Santa Cruz integrated zone (SA3), the southern expansion zone (SA4) and the northern expansion zone (SA5)

Although PLAND explained 88% of the variation in IIC in a simple linear model, a significant partial quadratic term (F 1110 = 810, P <0.0001) in a multiple linear model indicated that IIC changed in a nonlinear fashion with changes in PLAND. A nonlinear model (r 2 = 0.989; P < 0.0001) and a piecewise linear model (r 2 = 0.985; P < 0.0001; EQ. 3; Fig. 3) had coefficients of determination that exceeded 98%. Breakpoint values from Davies test (PLAND = 58.4; SE = 1.3; P < 0.0001) and piecewise linear regression (PLAND = 58.4; SE = 1.2; P < 0.0001) are very similar (Fig. 3). The piecewise linear regression showed that when PLAND cover was less than ~58%, connectivity increased 0.0036 (SE = 0.0003) units for each percent increase in PLAND; in contrast, when PLAND cover was greater than ~58%, connectivity increased 0.0161 (SE = 0.0006) units for each PLAND cover (Fig. 3). These two rates of change differed significantly below and above the breakpoint value (F 1110 = 192, P < 0.0001).
Fig. 3

Relationship between the percentage of natural vegetation (PLAND) and integral index of connectivity (IIC). Straight line represents the estimated regression equation based on a simple linear model (a). The dashed lines represent a piecewise linear regression model (where C = 58.4 is the threshold point and D is a dummy variable with D = 0 for C < 58.4 and D = 1 for C ≥ 1), and the vertical arrows represents the breakpoints identified using the Davies test (with ± se) (b)

Landscape spatial structure around jaguar observation data showed that jaguars used areas with mean PLAND = 83.14% (SE = 3.72%), mean patch density = 1.16 patches/100 ha (SE = 0.28 patches/100 ha), mean patch area = 616.95 ha (SE = 172.89 ha) and mean edge density = 705.27 m/ha (SE = 182.19 m/ha).

Discussion

Scale-based approaches are central to understanding the variability in the changes caused by deforestation and their potential effects on large carnivores’ habitat. Our study focused on assessing fragmentation and natural vegetation structural connectivity applying the spatial scale of habitat used by jaguars within the extent of human-modified landscapes. Our results showed that after 2001, areas with <58% (SE = 1.3) PLAND had a reduction of more than 80% in IIC values (i.e. connectivity) which may be a strong factor affecting jaguar abundance in the region. The results from the landscape spatial structure analysis based on jaguar observation data indicate that jaguars used areas with PLAND around 83% and with little fragmentation. The comparison of jaguar observation data landscape metrics with those from the sampling blocks after 2001 (Fig. 2) indicates that there may no longer be adequate natural vegetation to support resident female jaguars. The loss of natural vegetation has serious negative impacts on jaguar populations, but responses can be complex and could be exacerbated by further anthropogenic intervention on the landscape over long periods of time (De Angelo et al. 2013; Tobler et al. 2013; Kosydar et al. 2014). Fragmentation can cause a loss in connectivity with negative effects on food resources, female and/or male home ranges, flow of genetic material, subpopulations persistence and the probability of extinction risk (Saura and Pascual-Hortal 2007; Cavalcanti and Gese 2009; Reding et al. 2013). Losses in structural connectivity can also lead to losses in functional connectivity. As large patches of natural vegetation decrease, habitat for jaguar preys also decrease. Zimbres et al. (2017) showed that terrestrial mammal diversity depends on large tracts of natural vegetation along riparian corridors. Therefore, fragmentation due to agricultural activities would decrease jaguar habitat, prey habitat, and ecological function, thus limiting cover and food resources across the landscape. This also may increase the chances of human-wildlife encounter making jaguars more vulnerable to human activity and human intervention (Petracca et al. 2014; Olsoy et al 2016). Human expansion in our study areas will compete with jaguars for the availability of food resources through hunting and increase the potential for human-wildlife conflicts in ranching areas (Rumiz et al. 2012). Petracca et al. (2014) reported that 65% of people had negative attitudes and fear towards jaguars. Given this scenario, one important question to answer is how much deforestation can happen before jaguar population persistence is lost.

Our study revealed that once landscapes have <58.4% natural vegetation (SE = 1.3), structural connectivity within jaguar home ranges was lower than 20%. When PLAND was less than ~58%, connectivity only increased by 0.0036 units for each percent increase in PLAND while above ~58%, it increased by 0.0161 units (Fig. 3). This breakpoint value (and its standard error) in the relationship between PLAND and IIC could be interpreted as a threshold zone by which landscape permeability based on jaguar dispersal distances within the home range area may have been lost. Thresholds remain controversial, but there is evidence in theoretical and empirical studies that habitat loss has a significant effect on species occurrence within landscapes (Martensen et al. 2012; Johnson 2013). The existence of a threshold zone in structural connectivity may imply changes in natural vegetation dynamics, decreased availability of food resources, increased contact with humans and potentially human-jaguar conflicts (Altrichter et al. 2006; Rumiz et al. 2012; Young et al. 2014). Although jaguars may move across fragmented landscapes, they tend not to use small patches (<200 ha) as they prefer natural vegetation patches > 2000 ha (Ramirez-Reyes et al. 2016). In our study, we did not find mean patch sizes greater than 30 ha (±10.01) after 2001. This suggests that viable jaguar habitat may be very limited in the lowlands and foothills East of the Andes.

The significant loss of natural vegetation has potential implications not only at local scales but also at regional scales. Our study area is located at the centre of JCUs (Sanderson et al. 2002; Rabinowitz and Zeller 2010) and connects the jaguar populations of the Pantanal, the Chacoan region, the Eastern Andes, and the Amazon (Fig. 1a). Although there are several protected areas currently overlapping with JCUs (Fig. 1a), the land-cover changes reported in our study area are already likely to have caused a significant decline in connectivity over the past few decades. Given the amount of deforestation observed in the study area, it seems unlikely that conservation strategies that aim at increasing core habitat areas and/or maintaining structural connectivity will be effective in similar lowland tropical forests to maintain connectivity for large carnivore populations. Significant conservation efforts will be necessary to integrate habitat restoration and land management strategies that can significantly reduce or reverse current deforestation trends in these areas. Ecological threshold zones, such as the natural breakpoint observed in IIC and PLAND could potentially be used for conservation and management strategies to distinguish habitat management and conservation zones (areas above ecological thresholds), from areas where restoration will be needed (areas below ecological thresholds). Based on the land-cover database published by Killeen et al. (2007), our results show that loss of vegetation was greatest between 1991 and 2001. These changes have had greater significance during this time period due to social (e.g. large Andean migrations to the lowlands) and economic factors (e.g. soybean production, cattle ranching) that affected the country at a national scale (Killeen et al. 2007, 2008; Arancibia Arce et al. 2013). This is consistent with economic and social trends reported for our study area (Kaimowitz et al. 1999; Steininger et al. 2001a; Pacheco 2002; Killeen et al. 2008). Unfortunately, the trend has continued in the last decade with reported deforestation rates as high as 200,000 ha/year (Müller et al. 2014; Tejada et al. 2016) which is exacerbating poor habitat availability for wildlife in the lowland areas of Bolivia even more.

Conclusions

Although jaguars use a variety of habitats, land use change, habitat fragmentation and degradation can significantly affect their population persistence (Cavalcanti and Gese 2009). The impact of agriculture and the recent surge of gold mining in the Amazon (Tobler et al. 2013) are likely to exacerbate the reduction in vegetation extent with long-term impacts on jaguar population. This may be compounded by hunting and other potential human-jaguar conflicts (Altrichter et al. 2006; Rumiz et al. 2012). The effects of deforestation and loss of natural habitat are affecting a large number of species around the world. The use of relevant scales, that is the extent of human activity and their impact on species dispersal and home range, can be applied to a number of other species across the world where loss of natural habitat is affecting population abundance and survival. Our results also underscore the importance of providing scale-based approaches for assessing current conservation challenges to protect large carnivores and other species around the world.

Abbreviations

C: 

Estimated breakpoint

D: 

Dummy variable

Eq.: 

Equation

IIC: 

Integral index of connectivity

JCU: 

Jaguar conservation unit

PLAND: 

Percentage of natural vegetation cover

SA1: 

Study area 1

SA2: 

Study area 2

SA3: 

Study area 3

SA4: 

Study area 4

SA5: 

Study area 5

Declarations

Acknowledgements

The authors would like to thank the Department of Geography and the Geospatial Centre for Biodiversity at the Museo de Historia Natural Noel Kempff Mercado (Santa Cruz, Bolivia) for the provision of spatial databases that made this research possible. We thank Damián Rumíz for his insightful review of the manuscript and Dylan Young for his help in the threshold analysis.

Funding

No funding was provided for this research. Museo de Historia Natural Noel Kempff Mercado provided the necessary datasets for the completion of this project. Spatial databases for Bolivia were downloaded from the Bolivian Natural Resources Digital Center (http://cdrnbolivia.org/index.htm).

Availability of data and materials

Layers used for this study are available through the Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel René Moreno and at the Bolivian Natural Resources Digital Center (http://cdrnbolivia.org/index.htm).

Authors’ contributions

MAZ and HLP made substantial contributions to conception, design, and acquisition of data. All authors provided significant contributions to the analysis and interpretation of the data, and they have been involved in drafting the manuscript and revising it critically for important intellectual content. MAZ, HLP, ELD, ABG, JPL, and DBW have given final approval of the version to be published; they take public responsibility for appropriate portions of the content; and they have agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
School of Energy, Environment and Agrifood, Cranfield University
(2)
Institute of Ecology and Evolution, University of Bern
(3)
Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville
(4)
Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel Rene Moreno
(5)
Hensmans Farm, Nearton End
(6)
Cape Eleuthera Institute

References

  1. Altrichter M, Boaglio G, Perovic P (2006) The decline of jaguars Panthera onca in the Argentine Chaco. Oryx 40:302–309View ArticleGoogle Scholar
  2. Arancibia Arce LR, Perotto-Baldivieso HL, Furlán JR, Castillo-García M, Soria L, Rivero-Guzmán K (2013) Spatial and temporal assessment of fragmentation and connectivity analysis for ecotourism activities in a RAMSAR site: Banados de Isoso (Santa Cruz, Bolivia). Ecología en Bolivia 48:87–103Google Scholar
  3. Baggio JA, Salau K, Janssen MA, Schoon ML, Bodin O (2011) Landscape connectivity and predator-prey population dynamics. Landscape Ecol 26:33–45View ArticleGoogle Scholar
  4. Bradley AV, Millington AC (2008) Coca and colonists: quantifying and explaining forest clearance under coca and anti-narcotics policy regimes. Ecol Soc 13:31View ArticleGoogle Scholar
  5. Cavalcanti SMC, Gese EM (2009) Spatial ecolgoy and social interactions of jaguars (Panthera onca) in the southern Pantanal, Brazil. J Mammal 90:935–945View ArticleGoogle Scholar
  6. Cavalcanti SMC, Gese EM (2010) Kill rates and predation patterns of jaguars (Panthera onca) in the southern Pantanal, Brazil. J Mammal 91:722–736View ArticleGoogle Scholar
  7. Chazdon RL et al (2009) Beyond reserves: a research agenda for conserving biodiversity in human-modified tropical landscapes. Biotropica 41:142–153View ArticleGoogle Scholar
  8. Davies RB (1987) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 74:33–43Google Scholar
  9. De Angelo C, Paviolo A, Wiegand T, Kanagaraj R, Di Bitetti MS (2013) Understanding species persistence for defining conservation actions: a management landscape for jaguars in the Atlantic Forest. Biol Conserv 159:422–433View ArticleGoogle Scholar
  10. Delsink A, Vanak AT, Ferreira S, Slotow R (2013) Biologically relevant scales in large mammal management policies. Biol Conserv 167:116–126View ArticleGoogle Scholar
  11. Dixo M, Metzger JP, Morgante JS, Zamudio KR (2009) Habitat fragmentation reduces genetic diversity and connectivity among toad populations in the Brazilian Atlantic Coastal Forest. Biol Conserv 142:1560–1569View ArticleGoogle Scholar
  12. Gao P, Kupfer JA, Guo DS, Lei TL (2013) Identifying functionally connected habitat compartments with a novel regionalization technique. Landscape Ecol 28:1949–1959View ArticleGoogle Scholar
  13. Geist HJ, Lambin EF (2001) What drives tropical deforestation? A meta-analysis of proximate underlying causes of deforestation based on subnational case study evidence. Louvain-la-NeuveGoogle Scholar
  14. Gujarati DN, Porter DC (2009) Basic econometrics, 5th edn. McGraw-Hill, New YorkGoogle Scholar
  15. Horev A, Yosef R, Tryjanowski P, Ovadia O (2012) Consequences of variation in male harem size to population persistence: modeling poaching and extinction risk of Bengal tigers (Panthera tigris). Biol Conserv 147:22–31View ArticleGoogle Scholar
  16. Jackson VL, Laack LL, Zimmerman EG (2005) Landscape metrics associated with habitat use by ocelots in south Texas. J Wildlife Manage 69:733–738View ArticleGoogle Scholar
  17. Johnson CJ (2013) Identifying ecological thresholds for regulating human activity: effective conservation or wishful thinking? Biol Conserv 168:57–65View ArticleGoogle Scholar
  18. Kaimowitz D, Thiele G, Pacheco P (1999) The effects of structural adjustment on deforestation and forest degradation in lowland Bolivia. World Dev 27:505–520View ArticleGoogle Scholar
  19. Kendall MG, Stuart A (1966) Advanced theory of statistics, vol 3. Charles Griffin, LincolnGoogle Scholar
  20. Killeen TJ et al (2007) Thirty years of land-cover change in Bolivia. Ambio 36:600–606View ArticleGoogle Scholar
  21. Killeen TJ et al (2008) Total historical land-use change in Eastern Bolivia: who, where, when, and how much? Ecol Soc 13:36View ArticleGoogle Scholar
  22. Kindlmann P, Burel F (2008) Connectivity measures: a review. Landscape Ecol 23:879–890Google Scholar
  23. Kool JT, Moilanen A, Treml EA (2013) Population connectivity: recent advances and new perspectives. Landscape Ecol 28:165–185View ArticleGoogle Scholar
  24. Kosydar AJ, Rumiz DI, Conquest LL, Tewksbury JJ (2014) Effects of hunting and fragmentation on terrestrial mammals in the Chiquitano forests of Bolivia. Trop Cons Sci 7:298–317Google Scholar
  25. Kutner MH, Nachtsheim CJ, Neter J (2004) Applied linear regression models, 4th edn. McGraw-Hill, New YorkGoogle Scholar
  26. Lopez-Strauss H, Wallace RB, Mercado N (2010) Metodología para el levantamiento y sistematización de información sobre la distributción de mamíferos medianos y grandes en Bolivia. In: Wallace RB, Gomez H, Porcel R, Rumiz DI (eds) Distribución, ecología y conservación de los mamíferos medianos y grandes de Bolivia. Fundacion Simon I. Patino, Santa Cruz de la SierraGoogle Scholar
  27. Lorena RB, Lambin EF (2009) The spatial dynamics of deforestation and agent use in the Amazon. Appl Geogr 29:171–181View ArticleGoogle Scholar
  28. Maffei L, Cuellar E, Noss A (2004) One thousand jaguars (Panthera onca) in Bolivia’s Chaco? Camera trapping in the Kaa-Iya National Park. J Zool 262:295–304View ArticleGoogle Scholar
  29. Martensen AC, Ribeiro MC, Banks-Leite C, Prado PI, Metzger JP (2012) Associations of forest cover, fragment area, and connectivity with neotropical understory bird species richness and abundance. Conserv Biol 26:1100–1111View ArticleGoogle Scholar
  30. McGarigal K, Cushman SA, Neel MC, Ene E (2012) FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. University of Massachusetts, http://www.umass.edu/landeco/research/fragstats/fragstats.html
  31. Mertens B, Kaimowitz D, Puntodewo A, Vanclay J, Mendez P (2004) Modeling deforestation at distinct geographic scales and time periods in Santa Cruz, Bolivia. Int Regional Sci Rev 27:271–296View ArticleGoogle Scholar
  32. Millington AC, Velez-Liendo XM, Bradley AV (2003) Scale dependence in multitemporal mapping of forest fragmentation in Bolivia: implications for explaining temporal trends in landscape ecology and applications to biodiversity conservation. ISPRS J Photogramm 57:289–299View ArticleGoogle Scholar
  33. Minta SC (1992) Tests of spatial and temporal interaction among animals. Ecol Appl 2:178–188View ArticleGoogle Scholar
  34. Muggeo VMR (2003) Estimating regression models with unknown break-points. Stat Med 22:3055–3071View ArticleGoogle Scholar
  35. Muggeo VMR (2008) Segmented: an R package to fit regression models with broken-line relationships. R News 8:20–25Google Scholar
  36. Müller R, Pacheco P, Montero JC (2014) The context of deforestation and forest degradation in Bolivia: drivers, agents and institutions. Occassional Paper 108. CIFOR, Bogor BaratGoogle Scholar
  37. Neel MC (2008) Patch connectivity and genetic diversity conservation in the federally endangered and narrowly endemnoc plant species Astragalms albens (Fabaceae). Biol Conserv 141:938–955View ArticleGoogle Scholar
  38. Odland J (1988) Spatial autocorrelation. Sage Publications, New YorkGoogle Scholar
  39. Oliveira de Filho FJB, Metzger JP (2006) Thresholds in landscape structure for three common deforestation patterns in the Brazilian Amazon. Landscape Ecol 21:1061–1073View ArticleGoogle Scholar
  40. Olsoy PJ, Zeller KA, Hicke JA, Quigley HB, Rabinowitz AR, Thornton DH (2016) Quantifying the effects of deforestation and fragmentation on a range-wide conservation plan for jaguars. Biol Conserv 203:8–16View ArticleGoogle Scholar
  41. Pacheco P (2002) Deforestation and forest degradation in lowland Bolivia. In: Wood CH, Porro R (eds) Deforestation and land use in the Amazon. University Press of Florida, GainsvilleGoogle Scholar
  42. Pascual-Hortal L, Saura S (2006) Comparison and development of new graph-based landscape connectivity indices: towards the priorization of habitat patches and corridors for conservation. Landscape Ecol 21:959–967View ArticleGoogle Scholar
  43. Perotto-Baldivieso HL et al (2009) Spatial distribution, connectivity, and the influence of scale: habitat availability for the endangered Mona Island rock iguana. Biodivers Conserv 18:905–917View ArticleGoogle Scholar
  44. Perotto-Baldivieso HL, Wu XB, Peterson MJ, Smeins FE, Silvy NJ, Schwertner TW (2011) Flooding-induced landscape changes along dendritic stream networks and implications for wildlife habitat. Lanscape Urban Plan 99:115–122View ArticleGoogle Scholar
  45. Perotto-Baldivieso HL, Cooper SM, Cibils AF, Figueroa-Pagan M, Udaeta K, Black-Rubio CM (2012a) Detecting autocorrelation problems from GPS collar data in livestock studies. Appl Anim Behav Sci 136:117–125View ArticleGoogle Scholar
  46. Perotto-Baldivieso HL, Rivero K, Pinto-Ledezma J, Gill AB (2012b) Distributing biodiversity data through the web: the Geospatial Center for Biodiversity in Bolivia. EMBRAPA, BonitoGoogle Scholar
  47. Petracca LS, Hernández-Potosme S, Obando-Sampson L, Salom-Pérez R, Quigley H, Robinson H (2014) Agricultural encroachment and lack of enforcement threaten connectivity of range-wide jaguar (Panthera onca) corridor. J Nat Conserv 22:436–444View ArticleGoogle Scholar
  48. Rabinowitz A, Zeller KA (2010) A range-wide model of landscape connectivity and conservation for the jaguar, Panthera onca. Biol Conserv 143:939–945View ArticleGoogle Scholar
  49. Ramirez-Reyes C, Bateman BL, Radeloff VC (2016) Effects of habitat suitability and minimum patch size thresholds on the assessment of landscape connectivity for jaguars in the Sierra Gorda, Mexico. Biol Conserv 204:296–305View ArticleGoogle Scholar
  50. Reding DM, Cushman SA, Gosselink TE, Clark WR (2013) Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus). Landscape Ecol 28:471–486View ArticleGoogle Scholar
  51. Roques S et al (2016) Effects of habitat deterioration on the population genetics and conservation of the jaguar. Conserv Genet 17:125–139View ArticleGoogle Scholar
  52. Rudel T, Roper J (1997) Forest fragmentation in the humid tropics: a cross-national analysis. Singapore J Trop Geo 18:99–109View ArticleGoogle Scholar
  53. Rueda M, Hawkins BA, Morales-Castilla I, Vidanes RM, Ferrero M, Rodriguez MA (2013) Does fragmentation increase extinction thresholds? A European-wide test with seven forest birds. Global Ecol Biogeogr 22:1282–1292View ArticleGoogle Scholar
  54. Rumiz DI, Polisar J, Maffei L (2012) Memoria del Taller “El futuro del jaguar en el Gran Chaco”. SERNAP, PNANMI, Kaa, Santa Cruz de la SierraGoogle Scholar
  55. Sanderson EW, Redford KH, Chetkiewicz CLB, Medellin RA, Rabinowitz AR, Robinson JG, Taber AB (2002) Planning to save a species: the jaguar as a model. Conserv Biol 16:58–72View ArticleGoogle Scholar
  56. Saura S, Pascual-Hortal L (2007) A new habitat availability index to integrate connectivity in landscape conservation planning: comparison with existing indices and application to a case study. Lanscape Urban Plan 83:91–103View ArticleGoogle Scholar
  57. Saura S, Torne J (2009) Conefor Sensinode 2.2: a software package for quantifying the importance of habitat patches for landscape connectivity. Environ Model Softw 24:135–139View ArticleGoogle Scholar
  58. Scolozzi R, Geneletti D (2012) A multi-scale qualitative approach to assess the impact of urbanization on natural habitats and their connectivity. Env Imp Assess 36:9–22View ArticleGoogle Scholar
  59. Steininger MK, Tucker CJ, Ersts P, Killeen TJ, Villegas Z, Hecht SB (2001a) Clearance and fragmentation of tropical deciduous forest in the Tierras Bajas, Santa Cruz, Bolivia. Conserv Biol 15:856–866View ArticleGoogle Scholar
  60. Steininger MK, Tucker CJ, Townshend JRG, Killeen TJ, Desch A, Bell V, Ersts P (2001b) Tropical deforestation in the Bolivian Amazon. Environ Conserv 28:127–134View ArticleGoogle Scholar
  61. Superintendencia Agraria (2001) Cobertura de Uso Actual de la Tierra 2001. Centro Digital de Recursos Naturales de Bolivia. http://cdrnbolivia.org/geografia-fisica-nacional.htm
  62. Swift TL, Hannon SJ (2010) Critical thresholds associated with habitat loss: a review of the concepts, evidence, and applications. Biol Rev 85:35–53View ArticleGoogle Scholar
  63. Taylor PD, Fahrig L, Henein K, Merriam G (1993) Connectivity is a vital element of landscape structure. Oikos 68:571–573View ArticleGoogle Scholar
  64. Tejada G, Dalla-Nora E, Cordoba D, Lafortezza R, Ovando A, Assis T, Aguiar AP (2016) Deforestation scenarios for the Bolivian lowlands. Environ Res 144:49–63View ArticleGoogle Scholar
  65. Tobler MW, Carrillo-Percastegui SE, Zuniga Hartley A, Powell GVN (2013) High jaguar densities and large population sizes in the core habitat of the southwestern Amazon. Biol Conserv 159:375–381View ArticleGoogle Scholar
  66. Vanclay J, Kaimowitz D, Puntodewo A, Mendez P (1999) Spatially explicit model of deforestation in Bolivia. In: Laumonier Y, King B, Legg C, Rennolls K (eds) Data management and modelling using remote sensing and GIS for tropical forest land inventory. European Union, JakartaGoogle Scholar
  67. Vandermeer J, Lin BB (2008) The importance of matrix quality in fragmented landscapes: understanding ecosystem collapse through a combination of deterministic and stochastic forces. Ecol Complex 5:222–227View ArticleGoogle Scholar
  68. Visconti P, Elkin C (2009) Using connectivity metrics in conservation planning––when does habitat quality matter? Divers Distrib 15:602–612View ArticleGoogle Scholar
  69. Vogt P, Ferrari JR, Lookingbill TR, Gardner RH, Riitters KH, Ostapowicz K (2009) Mapping functional connectivity. Ecol Indic 9:64–71View ArticleGoogle Scholar
  70. Young D, Perotto-Baldivieso HL, Brewer T, Homer R, Santos SA (2014) Monitoring British upland ecosystems with the use of landscape structure as an indicator for state-and-transition models. Rangeland Ecol Manag 67:380–388View ArticleGoogle Scholar
  71. Zeilhofer P, Cezar A, Torres NM, Jacomo A, Silveira L (2014) Jaguar Panthera onca habitat modeling in landscapes facing high land-use transformation pressure-findings from Mato Grosso, Brazil. Biotropica 46:98–105View ArticleGoogle Scholar
  72. Zimbres B, Peres CA, Machado RB (2017) Terrestrial mammal responses to habitat structure and quality of remnant riparian forests in an Amazonian cattle-ranching landscape. Biol Conserv 206:283–292View ArticleGoogle Scholar
  73. Ziolkowska E, Ostapowicz K, Kuemmerle T, Perzanowski K, Radeloff VC, Kozak J (2012) Potential habitat connectivity of European bison (Bison bonasus) in the Carpathians. Biol Conserv 146:188–196View ArticleGoogle Scholar
  74. Zipperer WC (1993) Deforestation patterns and their effects on forest patches. Landscape Ecol 8:177–184View ArticleGoogle Scholar

Copyright

© The Author(s). 2017