Trends in fragmentation and connectivity of Paspalum quadrifarium grasslands in the Buenos Aires province, Argentina

Introduction

The loss of habitat and the fragmentation of ecosystems are one of the main threats to the conservation of biodiversity worldwide (Fahrig, 2003; Hobbs & Yates, 2003; Henle et al., 2004; Wilson et al., 2016; Woods et al., 2016). In Argentina, the conversion of natural ecosystems to agricultural lands has consequences such as the loss of habitat and biodiversity, the alteration of biotic interactions and biogeochemical processes (water cycles, carbon and nutrients), the reduction of the capacity to provide ecosystem services and the transformation of the landscape (Herrera, Texeira & Paruelo, 2013; Volante et al., 2012; Gandini, Lara & Scaramuzzino, 2014). In this way, and given the impact anthropogenic activities on natural systems (Vitousek et al., 1997), understanding if and how biodiversity recovers from disturbances is an important focus of ecology and conservation biology.

Despite its large distribution worldwide, only 4.6% of temperate grasslands are included within national protected area systems (Baldi et al., 2017). In Argentina, this situation is even more alarming since only 1.05% is protected (Bilenca & Miñarro, 2004). The underestimation of the productive value of these natural grasslands starts from the difficulty of objectively visualizing the goods and ecosystem services that they provide.

A characteristic type of grassland landscape in the Salado river basin is the “pajonal” of Paspalum quadrifarium also known as Paspaletum (Vervoorst, 1967). It represents one of the twelve plant communities identified in this area. It is a type of grassland characterized by a marked abundance of P. quadrifarium, a grass that can grow into dense tufts reaching 1 to 1.50 m high (Frangi, 1986), intermingled with various companion species in different proportions. Paspaletum is characterized by its distribution in a wide range of topographies (Lara & Gandini, 2013a) forming different vegetation units (Perelman, Burkart & León, 2003; Lara & Gandini, 2013b).

Paspaletum, like other pampa’s grasslands, has been under fire and grazing disturbance for a long time. Since the introduction of domestic livestock by European settlers and almost without interruption, this grassland has been managed by changing their coverage and land use in different ways (Foley et al., 2005; Vazquez, Zulaica & Requesens, 2012). Fire burning is currently used in the winter-spring period with the aim of increasing net productivity and thus livestock receptivity (Laterra, 2003). In this way, the interaction of fire with cattle grazing led to deep changes that can be seen across scales of analysis (Herrera et al., 2009; Lara & Gandini, 2011).

Ecologists distinguish between a particular disturbance event—like an individual storm or fire—and the disturbance regime that characterizes a landscape (Turner & Gardner, 2015; White & Jentsch, 2001). The current increase in disturbances due to agricultural and livestock pressure in the region (Cañibano, Gandini & Sacido, 2004; Vazquez, Zulaica & Requesens, 2012) is a discouraging scenario leading to the substitution of natural grass cover. However, Paspaletum (ecological community of Paja Colorada—Paspalum quadrifarium) remnant patches still persist in the centre of Buenos Aires province. These patches were maintained as pasture sites in good state of conservation (Herrera et al., 2009).

According to studies of “Fundación Vida Silvestre Argentina”—an environmental NGO—these sites were classified as Valuable Grassland Areas (PVAs), given their importance as a source of great native animal diversity and the numerous ecosystem services they provide (Bilenca & Miñarro, 2004).

Human encroachment on the environment through resource extraction and urban expansion have led to fragmentation (Maguire, Buddle & Bennett, 2016), with consequences for biodiversity (Chapin et al., 2000), ecosystem processes (Díaz & Cabido, 2001; Harrington et al., 2010) and the ecosystem services that they are supporting (Mitchell, Bennett & Gonzalez, 2014). Fahrig (2003) considered fragmentation as one of the most damaging threats to biodiversity conservation in recent times because the population viability in fragmented landscapes depends to a large extent on the structural and functional integrity of the landscape. In this context, it is necessary to carry out studies that allow analysing the trends of change at landscape scale in these habitat fragments, and their connections to implement management and conservation strategies in areas of high regional ecological importance and vulnerability.

The sustainable management of fragmented landscapes will depend on understanding the spatial ecology of the ecosystem services needed over the long-term (Maguire, Buddle & Bennett, 2016). In terms of the functional integrity of ecosystems, landscape connectivity is considered one of the key properties to maintain biodiversity. Landscape connectivity is defined as the degree to which landscape facilitates the movement of species and other ecological flows (Taylor et al., 1993). It is considered a key aspect to take into account for biodiversity conservation efforts around the world and one of the best responses to counteract the negative effects of habitat fragmentation and to facilitate species adaptation to changes in their natural habitats (Crooks & Sanjayan, 2006).

Improved landscape connectivity is becoming regarded as a viable management strategy to maintain biodiversity, ecosystem functions, and services (Ziter, Bennett & Gonzalez, 2013). In a part of the study area -the Salado River basin- the habitat fragmentation pattern has been reported to have increased considerably in the last 40 years (Lara & Gandini, 2014), thus becoming one of the major environmental problems in the basin.

Guidos Toolbox (GTB) is a free software available for analysing the structural and functional integrity of the landscape in terms of conservation. Particularly Morphological Spatial Pattern Analysis (MSPA) and Network Connectivity Analysis (NCA) can help researchers to build hypothesis about this issue. The generic setup of MSPA has been used to identify and map forest patterns, both structural (Vogt et al., 2006) and functional (Vogt et al., 2007), to identify key connectors for habitat suitability (Saura et al., 2011), for riparian corridor conservation studies (Clerici & Vogt, 2013), or evaluation of the US green infrastructure (Wickham et al., 2010) up to classifying zooplankton (Schmid et al., 2016). A unique feature of MSPA is the automatic detection of connecting pathways between core areas of image objects. Once found, the logical next point of interest is to rank those detected pathways with respect to the relative importance of each component, node and link in a given network. This task could be achieved by applying concepts and metrics of graph theory (Saura & Torné, 2009). Compared to typical Fragstat indices (Lara & Gandini, 2014), we consider that MSPA-exclusive information on core-areas, islets and connecting pathways could be the best way to show the changes along time. Complementarily, the direct relationship between MSPA and network theory presents NCA as the simplest way to explain the trends we search for. In addition, within GTB it is also possible to perform a calculation of the Equivalent Connected Area (ECA) as a summary of overall connectivity, because it has area units, it is easier to interpret and has a more usable range of variation (Saura et al., 2011).

In this work the MSPA and NCA in GTB were applied to a remote sensing classification of Landsat images in order to analyze the 42-year temporal change in the habitat fragments of Pq grasslands (Paspalum quadrifarium).

Materials & Methods

Study area

The study was carried out in the Flooding Pampa and Inland Pampa areas of the Salado River basin, in the Buenos Aires province (Argentina), covering mainly two different agroecological zones, the “Flooding and Inland Pampas” (Fig. 1). This square area contains most of the population of Pq and thus was considered as representative of the ecological community of interest (Gandini, Lara & Scaramuzzino, 2014) and their conservation status.

This phase of the work closely follows the methods previously described in Lara & Gandini (2014), with the addition of some data sources not used or not available for that work.

Results

The supplemental material provides a complete set of base images, and resulting images of Classification (Pq mask), MSPA and connectivity for the 5 analyzed years. To simplify interpretation, only the start and end years of the observation time period (1974–2016) are shown in the main body of the text.

Fragmentation, habitat loss and trends in connectivity

Table 1 summarizes the results of MSPA and Connectivity numerically. The small cores had a rapid fall of around 25% between 1974 and 1988, and then kept oscillating between values that never reached the initial stage. A similar behaviour was found for medium cores, which dropped by approximately 50% in the same time frame. The main change was found for large cores with a decrease of more than 60% in 1988, followed by a transient slight increase in 2000, and continued with the decreasing trends in 2011 and 2016.

The edges tend to decay oscillating until reaching a minimum value of 8.61 in 2011 and then recover slightly in 2016. Islets and perforations show a similar trend like cores.

Regarding the “linear” elements (loops, bridges and branches) a tendency to prevail in time is observed although the bridges are lost more quickly reaching low values from 2000.

The foreground area, reached a minimum value in 2011, recovering slightly in 2016 (Table 1, see also Figs. 2 and 3), although not achieving the previous values of 1974, 1988 or 2000.

Statistics of links showed mean values decreasing from 1974 to 2016, and maximum values recovering to a 50% of their original value. In terms of connectivity, the average number of links declined rapidly between 1974 and 1988, tending to decrease during the entire study period, although the percentage in relative ECA increased at a high rate and there was a minimum in 2011, indicating the dynamics of the patches.

Fragmentation measured as number of components (patches) resulting from habitat pressure was higher in 2011 and 2016 (Table 1). This observation agrees with MSPA data, in which the pajonal nuclei had their minimum representativeness in 2000, and recovered by 2011, with a significant percentage increase in the small patches (=islets). While edges and loops remained relatively stable, the linear elements (bridges and branches) increased their representativeness (Table 1, MSPA; see also Figs. 2 and 3). In 2016, larger corridors (mainly edges of roads) could be observed while the total connectivity of the landscape pattern remains low (Table 1; connectivity and Figs. 4 and 5) in terms of mean and median connectivity after 1988 and percentage of relative ECA after 2011.

Temporal variation of relative ECA showed the dynamics of the fragmentation process in the study area: from an initial value of 28%, almost doubled in only 14 years (1988). In successive years, the connectivity and ECA decreases to a minimum in 2011, followed by a slight increase in 2016. A visual comparison between connectivity maps (Figs. 4 and 5), showed spatially the relevance of the change in connectivity and network size.

Figure 6 shows the result of MCD applied between 1974 and 2016. A foreground loss of 43% is explained mainly as an internal foreground loss (elasticity 1.10575). In general, it could be observed a high Pq loss area and some gain areas in the west side of the map. Only a zone in the south-west of the study area remains less changed in the foreground.

Discussion

This work showed that the habitat patches of Pq have undergone deep transformations since 1974 in agreement with the findings of Lara & Gandini (2014). They reported that the original landscape matrix of Pq was replaced by other subordinate communities, e.g., Short-Grasses Matrix.

The variations in the size and connectivity of the patches of Pq were significant in the study period (1974–2016). The major effects were the habitat fragmentation and patch number and size reduction leading to connectivity loss. In terms of fragmentation, there was a true fragmentation, and also a slight habitat loss, determined by: (a) a high trend to lower the patch size and the connectivity and (b) an increase of linear elements at the end of the study period. Although the connecting elements of the landscape had maintained and/or recovered in percentage, favoring the dispersion of the present diversity in the habitat nuclei and preserving patches as breakup centers, this fact could be related with a degradation by edge effect.

Slight structural recovery measured by ECA% and in terms of area could be seen at the end of the study period mainly for the maintenance of linear structures such as bridges. Similar to fragmentation, connectivity is not a good or bad issue, and it strongly depends on the selected species we analyse but a negative trend is pointing at least some habitat quality loss for a species.

Statistics of links as mean, median and maximum, shows also the trends of connectivity and habitat losses. Means decreased continuously, and median values increase in 2016 along with the maximum number of links indicating a possible partial recovery.

The effect on diversity could be more important when considering that habitat patches form a “network” and that they are not connected to each other (Maguire, Buddle & Bennett, 2016). This loss of connectivity is clearly evidenced comparing Figs. 4 vs 5.

Although the connectivity (median values) of 1988 is maintained with respect to 1974, relative ECA increases to double. This would indicate that the dynamics of fragmentation occurred rapidly, starting with a decrease in the size of the patches, followed by a loss of connectivity. The ECA as calculated via GTB looks at the overall structural connectivity between a set of network components (Core + bridges of Pq) but excluding all the other MSPA classes, accounting for their area, number of nodes and links within each component. ECA is defined as the size that a single patch of habitat (maximally connected) should have in order to provide the same value of IIC (integral index of connectivity) or PC (Probability of Connectivity) than the actual habitat pattern in the landscape (Saura et al., 2011). With this definition, ECA provides an non species-specific measure and as such an independent overall measure of connectivity.

Within the framework of environmental conservation, monitoring these environments and conducting further research on their impact on biodiversity are deemed necessary. The methodological approach used in this work, like that employed by Wickham et al. (2010), was used to identify and classify the morphological types of fragmentation, focusing on the dynamics of habitat availability and also to recognize the temporal variation of the landscape connective elements.

This research opens up the need to face a short-term research about the minimum size of pajonal fragments that preserve different ecosystem services, maintaining their biodiversity components and structure in an acceptable status. Having this information, it is possible to propose sustainable management plans for the livestock agro-ecosystems of the region.

The results obtained with MCD also show that the community of Pq (managed as forage source in this case) tends to change spatially in a very dynamic way as shown clearly in Fig. 6. Zones without change are very scarce during the study. Only a portion of the study area in the south–west remains substantially unchanged in the foreground. This area has the potential to be taken as an area of research and also as example of livestock management, since it is the one that would most preserve the biodiversity of the Pq environment.

On the methodological side, the generic design of the fragmentation assessment scheme employed here for grasslands could be applied equally to assess and monitor dynamics of any other land cover type, i.e., fragmentation of forests or agricultural land.

Supplemental Information