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Regional Environmental Change

, Volume 18, Issue 6, pp 1765–1782 | Cite as

A landscape ecology assessment of land-use change on the Great Plains-Denver (CO, USA) metropolitan edge

  • Joan Marull
  • Geoff Cunfer
  • Kenneth Sylvester
  • Enric Tello
Original Article

Abstract

For better or worse, in those parts of the world with a widespread farming, livestock rising, and urban expansion, the maintenance of species richness and ecosystem services cannot depend only upon protected natural sites. Can they rely on a network of cultural landscapes endowed with their own associated biodiversity? We analyze the effects of land-cover change on landscape ecological patterns and processes that sustain bird species richness associated to cropland-grassland landscapes in the Great Plains-Denver metropolitan edge. Our purpose is to assess the potential contribution to bird biodiversity maintenance of Great Plain’s cropland-grassland mosaics kept as farmland green belts in the edge of metropolitan areas. We present a quantitative landscape ecology assessment of land-cover changes (1930–2010) experienced in five Great Plains counties in Colorado. Several landscape metrics assess the diversity of land-cover patterns and their impact on ecological connectivity indices. These metrics are applied to historical land-cover maps and datasets drawn from aerial photos and satellite imagery. The results show that the cropland-grassland mosaics that link the metropolitan edge with the surrounding habitats sheltered in less human-disturbed areas provide a heterogeneous land matrix were a high bird species richness exists. They also suggest that keeping multifunctional farmland-grassland green belts near the edge of metropolitan areas may provide important ecosystem services, supplementing traditional conservation policies. Our maps and indicators can be used for selecting certain types of landscape patterns and priority areas on which biodiversity conservation efforts and land-use planning can concentrate.

Keywords

Agro-ecosystems Land-cover/land-use change Ecological connectivity Landscape heterogeneity Bird species richness Great Plains 

Introduction

Can sustainable farm systems contribute to biodiversity conservation?

It is widely recognized that at a global scale industrialization of agriculture with the Green Revolution adopted from the mid-twentieth century onwards has been a major driver of biodiversity loss (Matson et al. 1997; Tilman 2002). At the same time, it is increasingly evident that well-managed agroecosystems can play a key role in biodiversity maintenance (Bengtsson et al. 2003; Tscharntke et al. 2005) by providing well-connected cropland-grassland mosaics that can maintain a relevant degree of species richness (Tress et al. 2001; Jackson et al. 2007). Depending on land-use intensities and the type of farming, agricultural systems may either enhance or decrease this biodiversity associated to cultural landscapes (Swift et al. 2004).

The loss of biodiversity is a focus of growing scientific and public concern (Schröter et al. 2005). The increasing impact of global land-cover and land-use change (LCLUC) challenges scientific research to develop new approaches that can better inform public policies worldwide (Turner et al. 2007). Landscape ecology provides useful quantitative tools for an environmental assessment of the impacts of these LCLUC (Li 2000), by studying the links between ecological patterns and processes (Verburg et al. 2009). Landscape ecology metrics can also test ecological models that identify land-cover spatial heterogeneity combined with intermediate levels of human disturbances as key mechanisms that explain how complex agroecological landscapes can maintain associated biodiversity (Marull et al. 2016). According to this approach, land-cover diversity differentiates habitats within cultural landscapes, and less human-disturbed patches offer shelter to species endowed with a variety of dispersal abilities which will attempt to recolonize the most disturbed lands (Loreau et al. 2010).

Our hypothesis is that large areas of intermingled patches of cropland and pasture create cropland-pastureland mosaics that could provide the kind of heterogeneous cultural landscapes able to host an important associated biodiversity (Altieri 1999). In turn, this wildlife-friendly mosaics (Tscharntke et al. 2012) can offer a much needed ecological connectivity with less human-disturbed areas where species can shelter, such as set-aside areas (Pino and Marull 2012). Bird observation will be used as a test, given that previous studies have pointed out the importance of heterogeneous landscapes for bird conservation (Boulinier et al. 1998). Many of these studies warn that grassland bird populations have declined throughout North America in recent decades (Brennan and Kuvlesky 2005; Hamer et al. 2006), whereas grassland heterogeneity restoration may counteract this trend (Fuhlendorf et al. 2006).

The goal of this article is to analyze the effects of the LCLUC on the landscape ecological patterns and processes that may sustain bird species richness associated to cropland-grassland landscapes in five Colorado counties linking northeast Denver’s metropolitan edge to the rural Great Plains between 1930 and 2010. The case study has been selected because it fits with all the characteristics needed to test our hypothesis, and also to take advantage of the large GIS dataset created for this area by the ICPRS Great Plains Population and Environment Data Series (https://www.icpsr.umich.edu/icpsrweb/ICPSR/series/207). Possible implications of the results for land-use policy and planning can be the assessment of the potential contribution to bird biodiversity conservation of the Great Plain’s cropland-grassland mosaics as green belts in the edge of metropolitan areas.

Main historical features of the land-cover change in the Great Plains, 1870–2010

We outline in this section a long-term perspective on how grassland and cropland evolved in the Great Plains from the start of its colonization in 1870 to the present, in order to better understand the role that the very few landscape patches which remained less human-disturbed throughout this time can play now for biodiversity conservation, as well as the relevance for this purpose of keeping them within a complex mosaic of heterogeneous land covers.

Land-cover patterns can adopt either a mosaic aspect (with a characteristic patchiness of distinct and intermingled covers) or a gradient appearance (with a seemingly continuous mixture of vegetal species that only gradually changes its spatial composition) (Forman 1995). Gradients were an outstanding feature of Great Plains grassland bioregions prior to Euro-American farm colonization in the late nineteenth century. Far from being a pristine wilderness, the assemblage of grasses and forbs were already cultural landscapes molded by Native Americans, mainly through fire regimes that increased bison populations hunted on foot or on horseback (Pyne 2001). Tallgrass, mixed grass, and shortgrass bioregions assembled a variety of species largely determined by rainfall gradients and soil capabilities, where bison grazing and prairie dog foraging imprinted even greater patchiness at a small scale. Watercourses opened within this grassland matrix some corridors of riparian vegetation (Dodds et al. 2004; Jones et al. 2010), where Native Americans occasionally seeded some crops and grew vegetable gardens (Hurt 1987; Fenn 2014).

Breaking the sod to begin widespread farming in the Great Plains entailed a strong socio-ecological transformation that caused a biodiversity decrease. Settlement devastated Native American cultures, together with a large share of the bison population, and the government placed both sets of survivors onto designated reserves (Cronon 1992; Isenberg 2000). The pioneer methods of farming depleted soil fertility through a soil mining process over several decades that only replenished small proportions of the nutrients extracted by crops (Cunfer and Krausmann 2009)—a true ‘metabolic rift’ (Fischer-Kowalski 1998; Schneider and McMichael 2010; Parton et al. 2015). Yet Euro-American settlers only plowed about 40% of the total land in the Great Plains during the pioneer era up to the 1930s (Cunfer 2005; Sylvester et al. 2016). By opening cropland patches within the remaining grassland matrix, and by replacing bison with cowherds as grazers, they created agroecosystems in a new cultural landscape. From then on, a key question for ecosystem services provision is how much biodiversity can be kept associated to these cropland-grassland agroecosystems (Derner et al. 2009; Euliss et al. 2010; Toombs et al. 2010; Drummond et al. 2012; Wright and Wimberly 2013; Sanderson et al. 2013; Joshi et al. 2017).

The path towards several types of mixed farming, more integrated with animal husbandry or ranching, was suddenly interrupted in the Great Plains by the extreme drought, dust storms, and economic depression of the 1930s (Cunfer 2005). This succession of crisis started an enduring era of public policies aimed at soil conservation, cropland set-aside incentives, and farmers’ income stabilization (Danbom 1995; Rosenberg and Smith 2009). Cropland expansion peaked in 1935 and has never returned to that level across the Great Plains. Over 16 million hectares of cropland were withdrawn from production each year between 1936 and 1942 in the USA, and efforts to reduce cropland were continued up to 1996 (Sylvester et al. 2016).

Yet the environmental impacts of these cropland retirement or set-asided policies are unclear, as they were combined with subsidies meant to boost farmers’ produce and coincided with major technological changes in agriculture: mechanization with diesel-powered tractors, synthetic fertilizers, chemical pesticides, hybrid seeds of dwarf wheat and other grains, a new system of intensive animal fattening with grain in feedlots, and the high-pressure irrigation pumps powered by internal combustion engines and electric motors (Opie 1993). Instead of pursuing and enhancing the agro-ecological improvements evident prior to the 1930s, industrialization of agriculture and livestock raising meant a set of drastic changes worldwide known as the “green revolution.” From a LCLUC standpoint the most salient feature was the new capability to bypass former natural limits to which farmers had previously learned to adapt (Cunfer 2005).

From the 1940s onwards, crop diversity decreased across the Great Plains, while feedlots meant livestock became less agro-ecologically integrated with the surrounding cropland and grassland. Conversions from grassland to crops have been higher in the dry western shortgrass bioregion than in the wetter eastern tallgrass zone, precisely because large unplowed areas remained there up to the 1940s, when chemical fertilizers, irrigation, and pesticides made them economically feasible. The places with most land-use change since the mid-twentieth century have been the cropland-grassland mosaics located between high cropland areas and metropolitan regions, where urban sprawl has encroached upon some of the best land, while farming and feedlots moved towards poorer soils (Wu 2000; Sylvester et al. 2016).

The general LCLUC patterns described fit well the trajectory of land use between 1930 and the present in five counties of northeastern Colorado, near the edge of the Denver metropolitan area. Besides localized and contrasting patterns of cropland intensification and grassland preservation, the pressures of urban sprawl have also affected the near-metropolitan area. This landscape ecology analysis of LCLUC applies landscape metrics that assess the structure and functionality provided by cropland-grassland mosaics that linked different sides of the study area, providing a land matrix able to maintain associated biodiversity. After a brief presentation of the study area and a description of the methods in “Methods,” we present the results in “Results.” First, land-cover changes are detailed, then a number of landscape properties are analyzed, and finally, the impact on birds’ species richness during the time frame is assessed. We discuss the results in “Discussion” before presenting our conclusions.

Methods

Study area and main cartographic sources

This analysis of the LCLUC in five counties (Weld, Logan, Morgan, Adams, and Arapahoe) near Denver, Colorado (Fig. 1), relies on a large GIS database built from agricultural censuses with detailed land-use information collected from 1860 to 2007 at 22 time points by the Great Plains Population and Environment Project (Gutmann 2005). In order to understand how changes in farming and livestock raising affected ecological processes, we calculate landscape ecology indexes using this GIS database to perform a Quantitative Landscape Ecology Assessment (QLEA) of the study area.
Fig. 1

Study area satellite scene in northeastern Colorado, and land-cover reclassification for 1992 and 2006. WRS coordinates (path: 33; row: 32); “Neutral” means unproductive. Source: our own from the National Land Cover Database (NLCD).

The land-cover maps of the satellite scene (National Land Cover Database—NLCD; WRS path 33 row 32) used covers a large area of northern Colorado, reclassified for land cover for 1992 and 2006 (Fig. 1). Within this scene, 40 sample cells of 5 × 5 km have been selected (eight sites were randomly selected for each of the five counties considered) so as to create an analysis at nested scales, with data for one entire satellite scene, for five counties within it, and for eight randomly selected sample cells within each county. The whole case study (Fig. 8 a) consists of these 40 sample cells within each satellite scene at five time points: 1930s, 1950s, 1970s, 1990s, and 2000s. For all of them, we present three types of metrics: landscape structure, landscape functionality, and land-cover change (see Table 3 in the “Appendix” for the definition and calculation of each metric).

Land-use change metrics

Three indicators reveal the nature of LCLUC over time (Table 3). The Land-Use Change (LUC) indicator measures the cell average of the land-use change of each pixel (Table 1), distinguishing between no change (0) and change (1) on a continuous range. The resulting LUC score reveals three stability regimes: stable (LUC = 0–0.2), semi-stable (LUC = 0.2–0.4), and non-stable (LUC = 0.4–1). Pressure (P) measures the percentage of pixels that change to urban or agriculture land use for each cell: no change (0); total change (1). Agriculture pressure Pa: low (Pa = 0–0.25); medium (Pa = 0.0.25–0.5); high (Pa = 0.5–0.75); very high (Pa = 0.75–1). Urban pressure Pu: low (Pu = 0–0.05); medium (Pu = 0.05–0.1); high (Pu = 0.1–0.2); very high (Pu = 0.2–1). Naturalness (N) measures the degree of preservation of grassland habitats. Five N levels are possible: grasslands (5), shelterbelts (4), pastures (3), crops and recently mowed areas (2), and urban areas, roads, and railways (1). See Table 3 for methodological details and associated references.
Table 1

Application of land-use change and landscape structure metrics in the sample cells at five time points, 1930s to 2000s

Land-use change

Code

Indicator

1930–1950s

1950–1970s

1970–1990s

1990–2000s

1930–2000s

LUC

Land-use change

0.225

0.134

0.164

0.135

0.358

LUC r

Land-use change regressive

0.004

0.007

0.005

0.006

0.012

LUC p

Land-use change progressive

0.008

0.003

0.003

0.002

0.006

Code

Indicator

1930s

1950s

1970s

1990s

2000s

P u

Urban pressure

0.011

0.014

0.012

0.014

0.018

P a

Agriculture pressure

0.363

0.412

0.421

0.489

0.402

N

Naturalness

3.232

3.694

3.678

3.473

3.720

Landscape structure

 (a) Total categories

  Code

Indicator

1930s

1950s

1970s

1990s

2000s

  LPI

Largest Patch Index (km2)

915

967

1028

929

960

  ED

Edge density (km)

57

77

64

65

69

  PD

Polygon density (n°)

55

59

40

45

52

  MESH

Effective mesh size (km2)

6.36

6.90

7.56

6.69

6.82

  H

Shannon Index

1.03

0.96

0.92

0.93

0.91

 (b) No urban categories (agriculture and grassland)

  Code

Indicator

1930s

1950s

1970s

1990s

2000s

  LPI

Largest Patch Index (km2)

915

967

1028

929

960

  ED

Edge density (km)

39

60

46

46

55

  PD

Polygon density (n°)

38

41

28

30

36

  MESH

Effective mesh size (km2)

6.47

6.98

7.63

6.77

6.90

  H

Shannon Index

0.88

0.81

0.80

0.86

0.84

 (c) Less disturbed categories (grassland alone)

  Code

Indicator

1930s

1950s

1970s

1990s

2000s

  LPI

Largest Patch Index (km2)

871

853

840

692

784

  ED

Edge density (km)

55

86

61

57

69

  PD

Polygon density (n°)

24

24

19

21

26

  MESH

Effective Mesh Size (km2)

7.20

7.20

7.09

5.60

6.20

  H

Shannon Index

0.17

0.16

0.12

0.29

0.27

The results show the average of all the sample cells (N = 40); See Table 6 for metrics description

Landscape structure metrics

Five indicators of land-cover heterogeneity and fragmentation reveal the ecological landscape patterns (spatial distribution of land covers) of the study area (Shannon 1948; Jaeger 2000) (Table 3). The Shannon Index (H) assesses land-cover equi-diversity.
$$ \mathrm{H}=\Sigma\ \left({\mathrm{P}}_{\mathrm{i}}\ {\mathrm{lnP}}_{\mathrm{i}}\right) $$
where Pi is the proportion of land matrix occupied by each type of land cover.
The Largest Patch Index (LPI) reports the area of the largest polygon (land-cover path) in each sample cell. Polygon Density (PD) indicates the number of polygons in each sample cell. Edge Density (ED) is the sum of the polygon perimeters in each sample cell. Effective Mesh Size (MESH) is the sum of the areas of the polygons squared, divided by the size of the study area, an indicator that can be interpreted as the inverse of landscape fragmentation.
$$ \mathrm{MESH}=\Sigma\ \left({A_i}^2\right)\ 1000/\Sigma\ \left({A}_i\right) $$
where Ai is the area of each polygon.

Landscape functionality metrics

Using the land-cover map of the satellite scene at two time points, 1992 and 2006 (Fig. 1), it is possible to calculate the Landscape Metric Index (LMI). The index is based on the landscape’s functionality (Turner et al. 2007) to support organisms and ecological processes (Table 3). LMI is calculated on the basis of four indicators: the capacity of relation between habitat patches, the ecotonic contrast between adjacent habitats, the human impact on habitats, and the vertical complexity of habitats (see Marull et al. 2007 for methodological details):
$$ {\displaystyle \begin{array}{l}\mathrm{LMI}=1+9\ \left({\gamma}_i-{\gamma}_{min}\right)/\left({\gamma}_{max}-{\gamma}_{min}\right)\\ {}\gamma ={I}_1+{I}_2+{I}_3+{I}_4\end{array}} $$
where γi is the sum of the indicators for each polygon in the region, while γmin and γmax are the minimum and maximum values, respectively. I1 is the potential relation, I2 is the ecotonic contrast, I3 is the human impact, and I4 the vertical complexity.

In order to calculate the Ecological Connectivity Index (ECI) the analysis changes scale, moving from the entire satellite scene (Fig. 1) to also include the 40 randomly selected 5 × 5 km sample cells nested within it (Fig. 8a). The index assesses the landscape functionality (Table 3) according to its ability to connect the horizontal flows of energy, matter, and information through the land matrix, which sustain biodiversity (Lindenmayer and Fischer 2007).

The diagnosis of ecological connectivity relies on defining a set of Ecological Functional Areas (EFA), which were considered the focal habitat patches to be connected, and a computational model of cost-distance of displacement, which includes the effect of modeled anthropogenic barriers (urban areas, infrastructures), considering the type of barrier, the range of distances and the kind of land use involved (see Marull and Mallarach 2005 for methodological details). This analysis uses GIS to apply the model to available historical land-use maps comprising the satellite scene and the whole set of sample cells. In order to establish the EFAs, the landscape categories were grouped according to habitat ecological affinity and then analyzed topologically (Andrén 1994; Bender et al. 1998). In those landscape categories still unable to generate simple EFAs, another topological analysis generated cropland-grassland mosaics using the same criteria described above.

The next step is to consider the anthropogenic barrier effects on landscape processes. An impact analysis of the space surrounding each barrier (roads and built-up areas) relies on a weighted classification of landscape polygons that act as barriers to ecological connectivity. The algorithm is based on a computational model of cost-distance in displacements, which includes a weight for each type of barrier and a potential matrix of land uses affected (Marull and Mallarach 2005). The model applies the CostDistance function in ArcGIS software and uses two databases: a “source” surface for each type of barrier (XBs; s = 1 ... 5) and an “impedance” surface from the potential matrix of areas affected (XA). This process results in a “ost-distance adapted” measure (d’s = bs – ds; where bs – ds > 0; being ds the cost-distance). Assuming that the effect of a barrier in YS point of the surrounding space is logarithmic, and decreases as a function of the distance (Kaule 1997), we have:
$$ {Y}_S={b}_s\hbox{--} {ks}_1\ \ln\ \left[{ks}_2\ \left({b}_s\hbox{--} {d^{'}}_s\right)+ 1\right] $$
where bs is the weight of each barrier, ks1 and ks2 are constants (adapting the graph to the distribution obtained using empirical data), and d’s is the cost-distance adapted for each barrier.
The barrier effect Y is defined as the sum of effects of all types and the cartographic expression obtained as a result is a surface:
$$ Y=\Sigma\ {Y}_s $$
The algorithm used to determine the ecological connectivity between landscape units applies a computational model of cost-distance, which considers the different classes of EFAs to connect and an impedance surface of land that includes a matrix of potential affinity, together with the effect of anthropogenic barriers. Again the model applies the CostDistance function using two databases: a “source” surface for each type of EFA (XC’r; r = 1 ... 3) and an “impedance” surface resulting from applying the effects of barriers to the potential affinity matrix (XI = XC’r + XY). The result is a cost-distance adapted to each type of functional ecological area (with d’r < 20,000 to avoid irrelevant information or concealment of results). By calculating the value of the sums of cost-distances adapted, this computational model of ecological connectivity defines a Basic Ecological Connectivity Index (ECIb):
$$ {ECI}_b=10\hbox{--} 9\ \left[\ln\ \left(1+{x}_i\right)/\ln\ {\left(1+{x}_t\ \right)}^3\right] $$
where xi is the value of the sum of the cost-distance by pixel and xt the maximum theoretical cost-distance.
Then ECIa is the Absolute Ecological Connectivity Index:
$$ {ECI}_a=\Sigma\ {ECI}_b/\mathrm{m} $$
where m is the absolute number of EFAs considered. This indicator emphasizes the role played by all sorts of agricultural and cropland-grassland mosaics in maintaining ecological connectivity (Pino and Marull 2012).

Bird species data as biodiversity indicator

Is there a link between landscape heterogeneity and biodiversity change? To test that possibility, this study employs as a first attempt of empirical underpinning the North American Breeding Bird Survey (BBS) data that provides bird observations from six driving routes within the study area. The BBS is an annual roadside survey of birds seen and heard along rural roads and secondary highways distributed throughout North America. Only the six BBS routes that fall within the satellite image scene from northern Colorado are used here (Fig. 8b). An expert who records all birds heard or seen within a 4-km buffer zone of the road has surveyed each of these routes annually in June since 1967. Data are summarized as a list of species reported on five stops along the route (Sauer et al. 2008). The five stops allow assembling the observations of the total number of bird species, and the specifically grassland bird species. BBS data come from 1991 and 2007 because they are the two time points with a larger sample, that broadly correspond with 1992 and 2006 satellite image land-cover data (given that annual LCLUC is practically undetectable on this scale).

The landscape structure of the study area has been analyzed from a raster generated by the NLCD using multispectral TM images obtained by Landsat 5 during 1992 and 2006 (“Study area and main cartographic sources”). In this section, eight different land-cover types are classified: water, urban, neutral (unproductive), forest, scrub, grassland/herbaceous, pasture/hay and croplands. These land-cover maps have been used to calculate landscape heterogeneity (H). This study then compares the effects of land-cover spatial distribution on local species richness of all birds (555 species), and of specifically grassland birds (27 species). The six BBS driving routes have been overlaid on the 30 × 30 m2 resolution indices’ maps to extract 4-km buffers around each route.

Farmland-associated biodiversity is much more than only birds. Yet, birds have some useful features as an indicator of the general quality of a farm environment (Boulinier et al. 1998). Their presence as predators and prey indicates the abundance of many other species upon which they depend. Birds can easily fly from less disturbed land to more disturbed landscape units where they find trophic resources. Finally, their populations have been better monitored than any other taxon. Hence bird observation data in the Denver study area can be used as a bio-indicator, and help us to bear out whether the ecological landscape metrics used are a modeling artifact, or whether they reflect actual values of landscape patterns and ecological processes (Hamer et al. 2006; Fuhlendorf et al. 2006). Yet, it must be acknowledged that the results of this empirical check can only be indicative, not conclusive.

Results

Land-use change results

The mosaic of crops amidst a grassland land matrix characterizes the predominant land-cover pattern of the Great Plains. The satellite scenes mainly show a general decrease in grassland and an increase in cropland and urban areas in northeastern Colorado between 1992 and 2006 (Fig. 2a), given that urban sprawl has tended to move the agricultural ring surrounding the metropolitan area further away (Sylvester et al. 2013). Conversely, the sample cell analysis based on aerial photograph interpretation (Fig. 2b) indicates a decline in cropland and increase in grassland cover on a wider scale during the last period of analysis (1990s–2000s), as the authors confirmed in fieldwork. The signature of plowing the land remains visible for a long time in air photo time series of these more distant areas (Sylvester and Rupley 2012). There is a homogenization of grasses during the recovery phase when croplands are abandoned or put into conservation reserve programs—since the disturbance signal is still visible in the lack of heterogeneity in the reflectance values of the satellite imagery (Maxwell and Sylvester 2012). The recovery of grassland vegetation in aerial photo imagery is less immediate than the multispectral signature visible in satellite imagery. Aerial photo interpretation can detect tillage in semi-arid grasslands for up to 50 years after cropland abandonment (McGinnies et al. 1991).
Fig. 2

Land-cover distribution in the satellite scene in 1992 and 2006, and in the sample cells in five time points from the 1930s to the 2000s

Due to the opposite trends experienced in the cropland ring nearer to Denver metropolitan edge and the more distant cropland-grassland into the Great Plains, some counties had very different land-use regimes (Fig. 3). The land-use change identified as LUCr indicates a change to urban and agricultural land uses. That identified as LUCp indicates a change to grassland land uses. The land-cover change carried out was more towards the former than the latter, except between the 1930s and 1950s (Table 1)—although changes depend on the scale of observation. The land-use pressure is high for agriculture (Pa) and low for urban uses (Pu) because sample cells are located mainly in non-urban areas (Table 1). In general, pressure (Pa) increased in the period analyzed throughout the agricultural ring surrounding the metropolitan area, and that fact coincides with a decrease in the habitats preserved in less disturbed land covers (N) (Table 4). This is clearly revealed by the strong correlation between Pa and N (−0.964; Table 6).
Fig. 3

a Land-Use Change (LUC). b Effective Mesh Size (MESH) indicators

Landscape structure results

LPI decreased in the period of analysis, mainly in grassland categories (Table 1). ED indicates the potential exchanges between land covers/land uses. The landscape ecotony changed in the period analyzed, with noticeably higher levels in the 1950s (Table 1). In general, there were not important changes in PD values. MESH is the inverse of the extent of this fragmentation, related to a lower grain size. There was an increase in landscape fragmentation in less human-disturbed categories such as grassland (Fig. 3). H measures land-cover equi-diversity, and it did not change, only revealing an increase in grassland categories (Table 1). At the same time, land-cover fragmentation (inverse of MESH) increased with time, at a rate equal to land-cover diversity (Table 5). This change is revealed by the correlation between MESH and H (− 0.759; Table 6). The ecological connectivity index (ECI) reveals how different land uses (H) are ecologically well-connected within the landscape in a mosaic structure (ED), rather than in a fragmented and unconnected pattern of land uses (MESH) because of the presence of barriers such as roads or urban areas (Table 6).

Landscape functionality results

The satellite scene analysis shows a decrease in the functional attributes of the landscape between 1992 and 2006, clearly assessed by the LMI as a measure of the capacity to maintain ecological processes and biodiversity associated to human-modified landscapes (Fig. 4a), which according to land-cover distributions seem to have been mainly caused by urban development and intensification of agriculture (Table 2). There was also a decrease in landscape connectivity (Fig. 4b), mainly located near urban areas. The size, topology, and mosaic structure of the EFAs influence connectivity and riparian corridors become very important in fragmented landscapes. Habitats tended to remain isolated from the rest of the land matrix due to the growing effect of anthropogenic barriers created around the metropolitan area.
Fig. 4

Landscape Metric Index (LMI) and Ecological Connectivity Index (ECI) applied in the satellite scene (red: higher indexes’ values) for 1992 and 2006. Landscape structure and connectivity dynamics (green: recovery; red: deteriorate) from 1992 to 2006

Table 2

Application of landscape functionality metrics in the satellite scenes, 1992 and 2006

Landscape Metrics Index (LMI)

LMI

Landscape category

1992

2006

ha

%

ha

%

0

No functional structure

184,166

5.8

242,880

7.7

1

Very low functional structure

177

0.0

1680

0.1

2

2605

0.1

7983

0.3

3

Low functional structure

19,676

0.6

17,191

0.5

4

30,814

1.0

35,588

1.1

5

Medium functional structure

142,956

4.5

140,336

4.4

6

255,385

8.1

661,552

20.9

7

High functional structure

577,137

18.2

859,961

27.2

8

1,854,517

58.6

1,187,685

37.5

9

Very high functional structure

96,929

3.1

11,757

0.4

10

2419

0.1

171

0.0

Total

3,166,783

100

3,166,783

100

Ecological Connectivity Index (ECI)

ECIa

Landscape category

1992

2006

ha

%

ha

%

1

No ecological connectivity

113,029

3.6

191,812

6.1

2

Low ecological connectivity

51,975

1.6

262,920

8.3

3

77,782

2.5

817,232

25.8

4

Medium ecological connectivity

403,539

12.7

873,428

27.6

5

1,327,255

41.9

653,030

20.6

6

High ecological connectivity

886,761

28.0

239,027

7.5

7

287,756

9.1

114,970

3.6

8

Very high ecological connectivity

18,675

0.6

14,359

0.5

9

10

0.0

4

0.0

10

0

0.0

0

0.0

Total

3,166,783

100

3,166,783

100

The results show correlations between the landscape change and landscape structure metrics used (with differences between the five counties and between years) and their influence on the QLEA (Table 6). Correlation analysis reveals the existence of different ECIb behaviors in the agro-ecosystems analyzed (ECI1 (agriculture), ECI2 (grassland), ECI3 (cropland-grassland mosaic), and ECIa (all categories). The results make apparent the importance of agriculture and cropland-grassland mosaics in jointly maintaining ecological connectivity (ECIa) of the study area.

Biodiversity results

The total number of birds observed declined significantly (P < 0.001; chi-square test) between 1967 and 2007 along routes located within the satellite scene (Fig. 5a), while the number of species increased between 1967 and 1990, but decreased thereafter (Fig. 5b). As only stops along the route that match in all years have been used to obtain this dataset, the results can only suggest some trends that require further research. The decrease in bird observations occurred after cropland (as measured in the sample cells) reached its greatest extent in the 1990s (Fig. 2b). Focusing only on the period between 1991 and 2007, just after the turning point described above, there was a decrease in the number of birds (Fig. 5a) and species (Fig. 5b) observed.
Fig. 5

a Number of bird observations. b Number of bird species, 1967–2007 and 1992–2007 (5 and 14 coincident route-stops, respectively)

There seems to be a positive correlation between landscape heterogeneity and bird species observations (Fig. 6a). Along the survey routes, the percentage of cropland-grassland mosaic decreased. The dominant habitat corresponded to a cropland-grassland mosaic along five routes, and cropland along one route, for 1991. In 2007, three routes had a cropland-grassland mosaic as the dominant habitat, cropland in two routes and urban in one route. In those routes where the dominant land cover changed to intensified cropland and urban covers, the number of species and individuals observed decreased. The number of different land-cover categories and the landscape heterogeneity also increased between 1991 and 2007 for all routes. Grassland bird species were more common in homogeneous landscapes (Fig. 6b), whereas the number of all kinds of bird species increased in heterogeneous cropland-grassland landscapes as measured by higher H′ values (Fig. 6a). Yet we also found a differentiated effect (chi-square test) depending on the percentage of grassland (P < 0.05; Fig. 7a) and cropland (P > 0.05; Fig. 7a), in accordance with the habitat needs of particular species.
Fig. 6

Relation between bird species richness and landscape heterogeneity (H′) a) in total species and b) in grassland species, for 1991 and 2007

Fig. 7

a) Relation between grassland bird species richness and percentage of grassland, and b) grassland bird species richness and percentage of cropland, for 1991 and 2007

Discussion

Land-cover change, landscape ecology, and biodiversity

A multi-scalar analysis of a land-cover dataset from the 1930s to the 2000s (Fig. 2) reveals a much more complex and dynamic pattern of LCLUC than the aggregated figures accounted only at a regional level (Hartman et al. 2011; Sylvester et al. 2013). Together with conversion of cropland to grassland, there have been many opposite trends of cropland expansion experienced in grassland located in marginal soils formerly considered unsuitable to plow (Sylvester and Rupley 2012). In some parts of the Great Plains, cultivation of poor soils has exceeded 1930s levels over the last 70 years. These land-use shifts in and out of cultivation can only be detected by zooming down to local scales, as we have done in our study areas (Fleischner 1994; Christian and Wilson 1999; Samson et al. 2004; Knopf and Samson 2013; Freese et al. 2014).

Grassland and cropland were the dominant land covers in 1992 (Fig. 2). However, a general loss of agricultural land cover during the next years to urban expansion, scrub, and forest reflected a combination of agricultural land-use conversion for social, economic or conservation reasons—e.g., participation in government incentives for farmers to convert highly erodible cropland to protective grassland cover. The grassland areas un-cropped and endowed with high species richness are only a fraction of the overall land matrix, and have become increasingly isolated amidst cropland-grassland mosaics (Fig. 4). For better or worse, a great deal of the species richness maintained at the bioregional scale depends on managed patches within cultural landscapes (Dodds et al. 2004; Fuhlendorf et al. 2006; Derner et al. 2009; Jones et al. 2010; Euliss et al. 2010; Toombs et al. 2010; Drummond et al. 2012; Wright and Wimberly 2013; Sanderson et al. 2013; Joshi et al. 2017). Studying the LCLUC patterns from a landscape ecology standpoint provides useful information to assess when, where, and why such cropland-grassland mosaics provide habitat differentiation that can be enhanced or reduced.

The results suggest a positive association between bird species richness and landscape heterogeneity, which deserves a deeper study in the future addressed to test the hypothesis that bird species richness tends to be lower in more homogeneous landscapes than in heterogeneous ones—except when specific grassland bird species are considered (Hamer et al. 2006; Derner et al. 2009; Joshi et al. 2017). These results indicate that rural cropland-grassland mosaics may provide farm-associated habitats and ecological connectivity that support a wider range of bird species richness (Bock et al. 1999; Boulinier et al. 1998; Fuhlendorf et al. 2006). Finally, increasing urban sprawl and transport infrastructures lead to a decrease in ecological connectivity (Fig. 4 and Table 2).

From a landscape ecology standpoint, the emergence in the Great Plains of increasingly integrated and complex agro-ecosystems, regionally adapted and differentiated, meant the consolidation of a diversity of land mosaics by 1930 (“Main historical features of the land-cover changes in the Great Plains, 1870–2010”). These cultural landscapes can still allow some degree of biodiversity maintenance (Tscharntke et al. 2005), by providing heterogeneous agro-ecological and pastoral land covers well connected with the differentiated habitats kept in less disturbed landscape patches (Agnoletti 2014).

The results highlight the key role played by cropland-grassland mosaics in maintaining the ecological functionality of the edge environments between the Great Plains and Denver metropolitan fringes (Fig. 4). They can provide a heterogeneous but permeable land matrix able to offer many habitats and a great deal of interconnectivity required to maintain an associated biodiversity, particularly once this biodiversity is no longer identified with wilderness (Cronon 1996).

Now these landscape mosaics are under pressure in the Denver area because of the combined effect of three ongoing land-use changes: urban sprawl, industrial agriculture, and cropland retirement linked to subsidy programs. Biodiversity is at risk especially due to the decrease in land-cover diversity and ecotones, as well as in viable ecological connectors. Further research, with more empirical data from different taxon is needed, to confirm or reject our hypothesis that cropland-pastureland mosaics can provide green infrastructures to maintain ecological processes and biodiversity near metropolitan areas of the Great Plains.

Policy implications

Several lessons can be learned from this landscape ecology assessment of land-use changes in the study area, with important implications for landscape and urban planning. First, land-use policy must consider the territory as a whole, taking into consideration the role of cropland-grassland mosaics in keeping a relevant degree of associated biodiversity. Only safeguarding National Parks and other protected areas that remain isolated in very far away locations is not enough to ensure the biodiversity-related ecosystem services that farmers, and society at large, require—pollination, pest and disease control, soil fertility maintenance, detoxification, water cleaning, and recreational (Millennial Ecosystem Assessment 2005; Kleijn et al. 2009; Butchart et al. 2010; Bommarco et al. 2013; van Zanten et al. 2014; Bridgewater et al. 2015; Andersson and Lindborg 2014; Herzog and Franklin 2016).

According to the well-known patch-corridor-matrix model (Forman 1995), agricultural colonization meant an increase in land-cover heterogeneity. New cropland-grassland mosaics replaced the previous continuous gradients in grassland diversity (see “Main historical features of the land-cover changes in the Great Plains, 1870–2010”). This combination of a spatially uneven disturbance with greater land-cover heterogeneity could offer more differentiated habitats to various species and ecological communities. As a result, β-diversity (species richness at landscape scale) increased, overriding the inevitable fall in α-diversity (at plot level) within plowed cropland—which is the typical impact of agroecosystem functioning on its own associated biodiversity (Gliessman 1990).

Second, in human-managed landscapes a cropland-grassland mosaic can provide an agro-ecological matrix able to nurture biological diversity—e.g., plant communities that retain native species; bees, butterflies, and other insects that perform a vital role as pollinators; many birds, and small mammals like prairie dogs whose burrows improve degraded soils and become a prey for carnivores such as the black-footed ferret, swift fox, golden eagle, American badger, and ferruginous hawk (Miller et al. 2000; Freemark et al. 2003; Lindsay et al. 2013). These managed cropland-grassland mosaics can also provide ecological connectivity to habitats sheltered in less-disturbed sites, sometimes linking them up to some protected areas. If farmlands and agro-pastoral mosaics are important contributors to habitat differentiation, and ecological connectors, this assessment and its associated maps provide useful information to identify critical points and priority areas for a land-use planning aimed at enhancing the ecosystem services that biodiversity provides in metropolitan areas (Dupras et al. 2016).

Third, the combination of the role played by cropland-grassland mosaics (measured by H′) as provider of habitats for farm-associated biodiversity, and as ecological connectors (measured by ECI) to avoid isolation between habitats, could contribute to biological conservation. Land-use and agricultural policies jointly addressed to develop wildlife-friendly ways of farming and livestock grazing may reinforce natural protection policy, establishing a land sharing approach to conservation that can reinforce the land sparing effort in creating National Parks (Fischer et al. 2008; Phalan et al. 2011). Such an approach would include avoiding urban sprawl and counteracting with corrective measures the barrier effect on ecological connectivity exerted by linear transport infrastructures.

Farm subsidies, mainly addressed thus far at sustaining farmers’ income and avoiding soil erosion, can also take into account these broader aims. Conservation policy can explicitly recognize the human-dominated nature of agricultural landscape mosaics and actively promote the design of biodiverse landscape features, edge habitats, and corridors of connectivity across heterogeneous land matrices that will extend up to natural spaces. Citizens, urban dwellers and politicians may understand that behind esthetic farm landscapes there are farmers who deserve to earn a fair income for their labor while managing and protecting spaces that provide many types of ecosystem services that become increasingly valuable near metropolitan areas. Urban planners and city dwellers can actively enhance these ecosystem services by connecting the metropolitan green belt of cropland-grassland mosaics with urban green areas and vegetable gardens, and treating all of them as a joint green infrastructure (Rouse and Bunster-Ossa 2013; Lovell and Taylor 2013; Green et al. 2016). Finally, by providing a long-term, dynamic perspective, the environmental history of the Great Plains landscape may also help to raise society’s awareness in order to adopt a broader approach to the sustainability of ecosystem services provision (Millennial Ecosystem Assessment 2005).

Conclusions

In the metropolitan fringes of the Great Plains near Denver, our case study, both people and nature live in human-dominated landscapes. The relevant question now is how much biodiversity can remain associated to the cropland-grassland mosaics of these cultural landscapes, so as to provide the vital ecosystem services that farmers and urban dwellers require.

It is widely recognized that at a global scale, industrialization of agriculture with the Green Revolution adopted from the mid-twentieth century onwards has been a major driver of biodiversity loss (Matson et al. 1997; Tilman 2002). At the same time, it is increasingly evident that well-managed agroecosystems can play a key role in biodiversity maintenance (Bengtsson et al. 2003; Tscharntke et al. 2005) by providing well-connected cropland-grassland mosaics that can maintain a relevant degree of species richness (Tress et al. 2001; Jackson et al. 2007). Depending on land-use intensities and the type of farming, agricultural systems may either enhance or decrease this biodiversity associated to cultural landscapes (Swift et al. 2004).

The results obtained by applying landscape ecology metrics to the LCLUC in the area northeast of Denver (CO, USA) confirm this twofold effect of farm systems on land-cover heterogeneity capable of hosting bird species richness. They suggest that keeping more integrated and wildlife-friendly cropland-grassland landscape mosaics may benefit and improve the biodiversity maintenance enhanced by conservation policies. To this end, grassland and pastureland patches have to be preserved from the extension of intensified cropland along the farmland belt around the metropolitan edge in order to avoid monocultures and keep a heterogeneous mosaic free from landscape fragmentation and barrier effects that jeopardize ecological connectivity. At the same time, in more distant areas where pastures are growing at the expense of cropland abandonment, a mosaic pattern can be maintained and improved by closer integration between the two land uses and extensive ranching.

These results are relevant for a better understanding of how the biodiversity associated to cultural landscapes is affected by agricultural intensification, urban sprawl, and transport infrastructure, as well as grassland recovery in retired former farmland. The main implication of the study for landscape and urban planning is the possible application of cropland-grassland mosaics as green belts in the Great Plains—metropolitan edges. This combination of positive and negative impacts of farming on biodiversity raises an interesting agenda for further research.

Notes

Acknowledgments

The authors would like to thanks the contributions of Myron Gutmann, Susan Leonard, Dan Brown, Melinda Smith, Mike Antolin, Dennis Ojima and Michael Brydge, and the technical support of Francesc Coll, Manel Pons and Marta Bayona. This work has been supported by the Social Sciences and Humanities Research Council of Canada, Partnership Grant 895-2011-1020, entitled “Sustainable Farm Systems: Long-Term Socio-Ecological Metabolism in Western Agriculture”.

Supplementary material

10113_2018_1284_MOESM1_ESM.docx (84 kb)
ESM 1 (DOCX 83.6 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Barcelona Institute of Regional and Metropolitan StudiesAutonomous University of BarcelonaBellaterraSpain
  2. 2.Department of HistoryUniversity of SaskatchewanSaskatoonCanada
  3. 3.Inter-University Consortium for Political and Social Research. Institute for Social ResearchUniversity of MichiganAnn ArborUSA
  4. 4.Department of Economic History and InstitutionsUniversity of BarcelonaBarcelonaSpain

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