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Ecological Health Index: A Short Term Monitoring Method for Land Managers to Assess Grazing Lands Ecological Health

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Grazing lands should be monitored to ensure their productivity and the preservation of ecosystem services. The study objective was to investigate the effectiveness of an Ecological Health Index (EHI) for assessing ecosystem ecological health in grazing lands. The EHI was developed by synthesizing existing vegetation and soil cover indicators. We implemented long-term transects at 44 farms from two ecological regions in Patagonia, the Humid Magellan Steppe (HMS) (n = 24) and Subandean Grasslands (SG) (n = 20), to collect data on established quantifiable vegetative and soil measurements and the EHI. Using known quantifiable measures, the HMS had numerically greater species richness compared to SG. Similarly, the average percentage of total live vegetation was more favorable in HMS. Correlating the EHI with these known quantifiable measures demonstrated positive correlations with species richness, the percentage of total live vegetation and carrying capacity and was negatively correlations with bare ground. These results suggest that EHI could be a useful method to detect the ecological health and productivity in grazing lands. Overall, we conclude that EHI is an effective short-term monitoring approach that ranchers could implement annually to monitor grazing lands and determine the impacts of ranch decision-making on important ecosystem indicators.
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environments
Article
Ecological Health Index: A Short Term Monitoring
Method for Land Managers to Assess Grazing Lands
Ecological Health
Sutie Xu 1, Jason Rowntree 1, *, Pablo Borrelli 2, Jennifer Hodbod 3and Matt R. Raven 3
1Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA; sxu@msu.edu
2Ovis 21, Belgrano 1585, Trevelin, Chubut 9203, Argentina; pborrelli@ovis21.com
3Department of Community Sustainability, Michigan State University, East Lansing, MI 48824, USA;
jhodbod@msu.edu (J.H.); mraven@msu.edu (M.R.R.)
*Correspondence: rowntre1@msu.edu; Tel.: +1-517-974-9539
Received: 7 May 2019; Accepted: 3 June 2019; Published: 10 June 2019


Abstract:
Grazing lands should be monitored to ensure their productivity and the preservation of
ecosystem services. The study objective was to investigate the eectiveness of an Ecological Health
Index (EHI) for assessing ecosystem ecological health in grazing lands. The EHI was developed by
synthesizing existing vegetation and soil cover indicators. We implemented long-term transects at
44 farms from two ecological regions in Patagonia, the Humid Magellan Steppe (HMS) (n =24) and
Subandean Grasslands (SG) (n =20), to collect data on established quantifiable vegetative and soil
measurements and the EHI. Using known quantifiable measures, the HMS had numerically greater
species richness compared to SG. Similarly, the average percentage of total live vegetation was more
favorable in HMS. Correlating the EHI with these known quantifiable measures demonstrated positive
correlations with species richness, the percentage of total live vegetation and carrying capacity and
was negatively correlations with bare ground. These results suggest that EHI could be a useful method
to detect the ecological health and productivity in grazing lands. Overall, we conclude that EHI is an
eective short-term monitoring approach that ranchers could implement annually to monitor grazing
lands and determine the impacts of ranch decision-making on important ecosystem indicators.
Keywords: grazing; ecological health; ecosystem indexes; Patagonia; grasslands; sustainability
1. Introduction
Grazing lands are necessary ecosystems to human life and occupy 3.6 billion ha or about one
third of global land area [
1
]. Grazing lands provide a range of provisioning ecosystem services such as
food, fiber and energy in addition to numerous regulating, cultural and supporting ecosystem services.
Because of population growth, urban sprawl and land-conversion, pastoral livestock production
systems have and will continue to experience challenges contextually to the natural resource base they
rely on, particularly land and water [
2
]. Hence, existing grazing lands must be managed for their
long-term productivity and health. Thus, knowledge and techniques assessing the impact (positive or
negative) of management of grazing lands on ecosystem services are critical.
To monitor the ecological health of grazing lands, several methods and techniques are used. These
monitoring strategies employ direct field sampling and lab measurement to obtain precise data of
specific ecosystem properties such as vegetation composition and soil organic matter [
3
6
]. However,
the ecological processes (water cycle, energy flow and nutrient cycle) and their interrelationships
are very complex which make it dicult or expensive to directly measure, particularly by land
managers [
7
]. Moreover, spatial and temporal variability in extensive grazing management systems
Environments 2019,6, 67; doi:10.3390/environments6060067 www.mdpi.com/journal/environments
Environments 2019,6, 67 2 of 18
adds diculty in detecting changes across landscapes [
8
]. Other techniques such as satellite remote
sensing or modeling are often used to evaluate ecosystem services or processes (vegetation cover, soil
erosion, runo, productivity, rain use eciency, carbon and nitrogen fluxes, etc.) and their subsequent
relationship with climate conditions and land use management [
8
14
]. These approaches require
professional expertise of well-trained scientific researchers and this expertise is not easily transferred
to land managers and herders. Another evaluation method demonstrated by the USDA NRCS utilizes
17 indicators to monitor ecosystem health and is easier for land managers to use [
7
] but this procedure
was also developed for use by experienced, rangeland professionals—not land managers. Therefore,
it is imperative that inexpensive monitoring approaches that maintain ecacy but are easy to learn
and comprehend for farmers and ranchers, continue to be improved upon.
To assess grazing land ecological health, ecosystem processes are often valued through life cycle
assessment or environmental footprints, such as carbon, water, energy, nitrogen and biodiversity [
15
].
To simplify ecosystem function assessment, managers can assess soil and site stability, hydrologic
function and biotic integrity by selecting a series of indicators such as bare ground, vegetation
composition, litter movement, soil loss, plant functional groups, water flow patterns, plant mortality
and invasive plants [
16
20
]. Further, Mitchell [
21
] summarized that plant and animal resource
indicators (e.g., plant species and communities) productive capacity indicators (e.g., annual primary
productivity, number of livestock, wildlife, etc.) or soil quality indicators (e.g., soil organic matter,
etc.) are useful to evaluate the sustainability of grazing lands and the supply of ecosystem services.
In grazing lands, indicators interconnected between livestock activities and landscape function are
particularly useful, such as the vegetation and soil patch attributes [22].
Beginning in 1990 and later published by Borelli and Oliva [
23
] land monitors began to utilize a
generic land-monitoring scorecard representing eight ecological indicators including functional group
assessment, however the method included no indicators on mineral cycling and energy flow. Later,
the qualitative grazing lands assessment method described by Pyke et al. [
16
] and Pellant et al. [
17
]
was adopted and interfaced into the original monitoring scheme of Borelli and Oliva [
23
]. Importantly,
Pellant et al.’s [
17
] method first described a ‘reference area’ as a monitoring area within an ecoregion
which exhibits high-functioning ecosystem processes. Further they provided more indicators relative to
live canopy, bare soil and evidence of plant species (desirable, undesirable and rare).
Pellant et al. [17]
also provided a scaled scoring approach that ranks each indicator on the amount of departure from
the reference area (none to slight, slight to moderate, moderate, moderate to extreme and extreme).
However, the overall methodology still lacked indicators of mineral cycling, soil erosion and plant
vigor. Further as the scoring was relative to the reference area, a monitor could not provide quantitative
scoring for how the ecological indicators functioned at the land surface.
Within this manuscript we present an ecological monitoring strategy, the Ecological Health Index
(EHI), which has been developed over a 20-year period and practiced on 2 million ha of land in
Argentina. The objective of EHI is to provide land users a quick, inexpensive method that uses
biological indicators that have predictive value of ecosystem function namely biodiversity, energy flow
and the mineral and water cycles. We adopted components of the Landscape Function Analysis (LFA)
by Tongway and Hindley [
24
], along with elements from Pyke et al. [
16
], Pellant et al. [
17
] and Borelli
and Oliva [
23
], to provide greater strength in quantifying land use change. Specifically, LFA is a visual
assessment procedure employing quickly determined field indicators related to soil properties and
ecosystem processes. Importantly, the method allows for greater assessment of litter composition and
for landscape function indices derived from indicator scoring to be grouped by ecosystem process.
The LFA has been used to assess soil function under grazing management, ecological restoration or
rehabilitation and has been shown to be an eective monitoring tool [
25
27
]. One challenge to the
monitoring strategy is the use of Canfield lines [
28
], a line interception method that requires measuring
of plant distance to an intercept among other calculations which can be time consuming.
Therefore, integrating the work of Pyke et al. [
16
], Pellant et al. [
17
], Borelli and Oliva [
23
] and
Tongway and Hindley [
24
], the EHI is a visual assessment method encompassing field indicators
Environments 2019,6, 67 3 of 18
which are closely related to ecosystem processes and can also be quickly measured (requiring about
five minutes per checkpoint). Moreover, the selected indicators for EHI, such as the species richness
(the number of species in the checkpoint), are simple in scope and thus hopefully more apt to be
adopted by farmers and managers.
Another component to our ecosystem indicator selection was the fact that those primarily involved
in its development are also accredited educators for holistic management which teaches the evaluation
of four ecosystem processes (water cycle, mineral cycle, energy flow and community dynamics) at
the landscape surface. Thus, we also worked to use language and variables that existing holistic
management practitioners associate with.
Before applying a new land monitoring strategy, it is essential to test its feasibility and reliability
by comparing the index score to other quantifiable measurements (QM) that are known ecological
indicators. For example, LFA indices (e.g., stability, infiltration and nutrient cycling index) have been
shown to positively correlate with soil properties (e.g., aggregate stability, water infiltration and soil
respiration) [
24
,
25
]. Therefore, our objective was to determine the eectiveness of EHI on assessing
the ecological health of grazing lands. To do this, we compared EHI and known QM in the literature,
resulting in ecological health comparisons between two ecoregions encompassing 44 grazing farms
across 398,949 ha. We also assessed the correlation between EHI, QM and carrying capacity for insight
into EHI eectiveness. Our hypothesis is the EHI is correlated to QM and carrying capacity and thus
can used as an eective method or grazing land monitoring.
2. Materials and Methods
2.1. Experimental Site
The study was conducted between 2011 and 2014 at 44 farms in two ecoregions of Patagonia: the
Humid Magellan Steppe (HMS) (n =24; 279,595 ha) and the Subandean Grasslands (SG) (n =20; 119,354 ha).
During the monitoring period, HMS had an annual average temperature of 5.8
C and ~300 mm rainfall.
The annual average temperature for SG was 7.9 C and average rainfall ~345 mm (Table 1).
Table 1.
Climate and monitoring information of the Humid Magellan Steppe (HMS) and Subandean
Grasslands (SG) ecoregions.
Farm Name Hectarage
(Ha)
Monitored
Subsidiaries
within Farm
Year of
Monitoring
Average
Rainfall
(mm)
Temperature
(C)
HMS
Monte Dinero 8000 1 2011 270 6.5
Morro Chico 26,025 2 2012 300 5.5
Namuncura 22,135 4 2012 250 5
Rupai Pacha 25,000 2 2012 200 5
Viamonte 40,600 1 2012 400 5
Punta Delgada 93,000 6 2013 250 6.5
Teraike 29,605 3 2013 400 6
Pamela Christian 9018 3 2013 400 6
Armonia 8000 2 2014 350 6
SG
La Paulina 4306 2 2012 250 8
Numancia 23,000 5 2012 400–600 8
Bajada de los Orientales 56,000 3 2012 200 7
El Amanecer 4661 1 2012 350 8
El Cronometro 4746 2 2012 350 8
La Legua 2500 1 2012 250 8
Montoso 2500 1 2012 250 9
Media Luna 1200 1 2012 350 8
Bajada de los Orientales 58,000 1 2013 180 5.8
Fortin Chacabuco 4382 3 2014 400 8
Environments 2019,6, 67 4 of 18
2.2. Ecological Health Index
In this study, EHI was developed as an adaptation of previous work [
17
,
23
,
24
]. Ecological
properties and processes including soil stability, water cycle, nutrient cycle, plant community dynamics
and energy flow were evaluated annually using multiple indicators determined by visual assessment
(Table 2, Appendix A). Each EHI indicator and the ecosystem processes they influence are referenced
in Table 2.
Table 2.
Ecological processes and related indicators evaluated for Ecological Health Index (EHI).
The Type columns explains if the indicator is evaluated as compared to the reference area or as a
stand-alone, absolute indicator. The latter four columns indicate which of the key four ecosystem cycles
this is an indicator of (white, not an indicator; gray, an indicator).
# INDICATOR UNIT Source Type Water
Cycle
Mineral
Cycle
Energy
Flow
Community
Dynamics
1Live Canopy
Abundance
Total green biomass
production/Site
potential
[24,29]
Ref. Area
2Living
Organisms
Evidence of
microfauna [24,30] Absolute
3FG 1—Warm
Season Grasses
Vigor, reproduction,
crown integrity [24,29,30] Absolute
4FG 2—Cool
Season Grasses
Vigor, reproduction,
crown integrity [24,29,30] Absolute
5FG
3—Forbs/Legumes
Vigor, reproduction,
crown integrity [24,29,30] Absolute
6FG 4—Desirable
Trees/shrubs
Vigor, reproduction,
crown integrity [24,29,30] Absolute
7
Contextually
Desirable Rare
Species
Frequency [24]
Ref. Area
8
Contextually
Undesirable
Species
Abundance [24,29,30]
Ref. Area
9Litter
Abundance % Cover [23,24,2931]
Ref. Area
10 Litter
Incorporation
Litter type, Soil
contact [23,24,2931] Absolute
11 Dung
Decomposition
Dung
Disappearance rate [24] Absolute
12 Bare Soil % Bare soil [23,24,2931]
Ref. Area
13 Capping Soil surface
resistance [23,24,29,31] Absolute
14 Wind Erosion Blowout/Deposition [23,24,2931] Absolute
Active pedestals [23,24,2931] Absolute
15 Water Erosion Rills/water flows [23,24,2931] Absolute
Gullies [23,24,2931] Absolute
To use the EHI, an evaluation matrix was created for each ecoregion, following the procedures
proposed by Pellant et al. [
17
]. For each ecoregion, we first identified two or more reference areas—the
described, best-known expression of biodiversity, site stability and ecosystem function or a site
considered most representative of the grazing land’s ideal state. The evaluation matrix for reference
areas in HMS and SG are presented in Appendix A. A reference area score card was developed
according to the ecoregion evaluation matrix. At each participating farm, scores were assigned to each
indicator using the score card. The EHI is the cumulative score of for all indicators ranging from
130
to +110. Higher EHI indicates greater ecosystem function while low values suggest that the ecosystem
Environments 2019,6, 67 5 of 18
is a low-functioning landscape with considerable negative departure from the reference area in that
Ecological Region.
Besides EHI, the functional indexes including Soil Stability Index (SSI), Water Cycle Index (WCI),
Nutrient Cycle Index (NCI), Plant Community Dynamics Index (CDI), Energy Flow Index (EFI) were
calculated using related indicators (Table 2) from the following equation adapted from Tongway and
Hindley [24]:
I=1(M i)/D
where
I=Index value (SSI, WCI, NCI, CDI or EFI),
M=Max possible value of the total scores of related indicators,
i=total scores of related indicators,
D=Dierence between max and min possible values of the total scores of related indicator.
The value of each functional index reflects the ecosystem cycle function observed at the monitored
site in comparison to the ideal condition of the reference area. For example, if the WCI value is
100%, this indicates the assessed water cycle is similar to the best expected condition for water in the
monitored area.
2.3. Long Term Fixed Transects
The methodology for long term fixed transects was adapted from Oliva et al. [
32
] for assessing
grazing lands in Patagonia [
32
]. Transects can be used for long-term monitoring (every 4 to 5 years).
The aim is to track the change in ecosystem process functionality over time using QM, an addition to
the short-term attributes measured with EHI. In this study, the long-term fixed transects were used
to assess QM and EHI in the same years, so as to compare these two monitoring methods. At each
monitoring farm a fixed transect was installed (Figure 1) and measures were taken to assess QM
including species richness, the Shannon-Wiener Index, percentage area of bare ground, litter, standing
dead (dead material not in contact with the land surface), evidence of cryptogams or ephemeral
species and total live vegetation abundance. These QM aimed to reflect the soil surface and vegetation
composition as ecosystem function indicators. Specifically, the basal cover and plant biodiversity was
read using a Point and Flexible Area Method (PAF) [
33
]. This method is used for rapid inventories of
ecological function status combines classical methods of line point intercepts with quadrat sampling
areas. As indicated in Figure 1, two-line point transects (transect 1 and 2) of 25 m long, points spaced
every 0.25 m (100 points in each transect, total 200 points) were used to do quantifiable assessment of
plant species. At each point, a pin was pointed to the ground and the plant species touched by the pin
were recorded. Also, a variable area on each side of T2 (flexible quadrat) was used to check for rarer
species. All species not recorded by pin hits included in the area between T1 and T3 were recorded, with
their basal area and distance to T2. A complete species list with quantifiable cover estimates was then
obtained. Species litter or bare ground observed from the 200 points were recorded. This procedure
gives an estimate of biodiversity indicators (species richness and Shannon-Wiener Index) and describes
canopy cover by species and functional groups. The T3 was used for observation in ten 50
×
50 cm
quadrats, spaced every 2 m and the EHI was evaluated and recorded. Each participating farm was
surveyed to assess their carrying capacity. The carrying capacity data corresponds to paddock average,
was defined as sheep animal days/hectare and was compared with EHI.
Environments 2019,6, 67 6 of 18
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Figure 1. Long-term monitoring transects.
2.4. Statistical Analysis
Linear correlation between EHI, QM and carrying capacity and between functional indexes (SSI,
WCI, CDI and EFI) and QM was conducted using SAS PROC CORR procedure (SAS 9.4, 2013). The
linear regression analysis was conducted using SAS PROC REG procedure with EHI as the
independent variable and QM as the dependent variable.
3. Results
3.1. Ecological Health Index and Quantifiable Measurements
Both HMS and SG had 2 farms with EHI values greater than 40, indicating they are ecologically
high functioning. However, 11 farms in SG (55% of 20 farms) and 6 farms in HMS (25% of 24 farms)
had negative EHI (<0), suggesting a low ecosystem function.
In order to assess EHI efficacy, we compared the EHI farm score to multiple QM in each
ecoregion and analyzed the correlation between EHI and selected QM. The QM included species
richness, the Shannon-Wiener Index (which refers to the diversity and evenness of plant species) and
the percentage area of bare ground, litter, standing dead, cryptogams, ephemeral species and total
live vegetation. In each ecoregion, EHI was positively correlated with species richness (R = 0.61, P =
0.0022 for HMS, Figure 2; R = 0.67, P = 0.0022 for SG, Figure 3).
No significant correlations were detected between EHI and the Shannon-Wiener Index (data not
shown). The Shannon Wiener Index was 1.6 to 2.5 for HMS and 0.6 to 2.4 for SG, respectively. The
HMS and SG had 0 to 11.7% and 0 to 19.5% standing dead respectively and 0 to 9.5% and 0 to 3.5%
cryptogams respectively, none of which was correlated with EHI. The percentage of ephemeral
(short-lived, non-perennial) species was lower than 6% and no correlations with EHI were detected.
Perennial species play an important role on regulating ecosystem services, even on annual dominated
ecosystems [34]. In this study, the percentage of cryptogams and ephemeral species are relatively low
so both ecoregions were dominated by perennial grasses.
Figure 1. Long-term monitoring transects.
2.4. Statistical Analysis
Linear correlation between EHI, QM and carrying capacity and between functional indexes
(SSI, WCI, CDI and EFI) and QM was conducted using SAS PROC CORR procedure (SAS 9.4, 2013).
The linear regression analysis was conducted using SAS PROC REG procedure with EHI as the
independent variable and QM as the dependent variable.
3. Results
3.1. Ecological Health Index and Quantifiable Measurements
Both HMS and SG had 2 farms with EHI values greater than 40, indicating they are ecologically
high functioning. However, 11 farms in SG (55% of 20 farms) and 6 farms in HMS (25% of 24 farms)
had negative EHI (<0), suggesting a low ecosystem function.
In order to assess EHI ecacy, we compared the EHI farm score to multiple QM in each ecoregion
and analyzed the correlation between EHI and selected QM. The QM included species richness,
the Shannon-Wiener Index (which refers to the diversity and evenness of plant species) and the
percentage area of bare ground, litter, standing dead, cryptogams, ephemeral species and total live
vegetation. In each ecoregion, EHI was positively correlated with species richness (R =0.61, P=0.0022
for HMS, Figure 2; R =0.67, P=0.0022 for SG, Figure 3).
No significant correlations were detected between EHI and the Shannon-Wiener Index
(data not shown). The Shannon Wiener Index was 1.6 to 2.5 for HMS and 0.6 to 2.4 for SG, respectively.
The HMS and SG had 0 to 11.7% and 0 to 19.5% standing dead respectively and 0 to 9.5% and 0 to
3.5% cryptogams respectively, none of which was correlated with EHI. The percentage of ephemeral
(short-lived, non-perennial) species was lower than 6% and no correlations with EHI were detected.
Perennial species play an important role on regulating ecosystem services, even on annual dominated
ecosystems [
34
]. In this study, the percentage of cryptogams and ephemeral species are relatively low
so both ecoregions were dominated by perennial grasses.
Environments 2019,6, 67 7 of 18
Environments 2019, 6, x FOR PEER REVIEW 7 of 18
Figure 2. Linear regression for HMS between EHI and QMs species richness (R = 0.61), cover
percentage of total live vegetation percentage (R = 0.69), bare ground (R = 0.72) and litter (R = 0.27).
To further confirm the effectiveness of EHI, we calculated functional indices (SSI, WCI, NCI CDI
and EFI) by selecting indicators used for EHI associated with soil stability, water cycle, nutrient cycle,
plant community dynamics and energy flow (see Table 2). The correlation analysis between the
functional indices and QM are indicated in Table 3. Similar to EHI, the SSI, WCI, CDI and EFI were
generally positively correlated to species richness and the percentage of total live vegetation and
negatively correlated with percentage of bare ground, at both ecoregions. Specifically, SSI, WCI and
EFI were highly correlated with the percentage of total live vegetation (R = 0.69 to 0.85) and bare
ground (R = 0.74 to 0.93), indicating soil stability, water cycle and energy flow are potentially
effective indicators for ecological function.
Figure 2.
Linear regression for HMS between EHI and QMs species richness (R =0.61), cover percentage
of total live vegetation percentage (R =0.69), bare ground (R =0.72) and litter (R =0.27).
To further confirm the eectiveness of EHI, we calculated functional indices (SSI, WCI, NCI
CDI and EFI) by selecting indicators used for EHI associated with soil stability, water cycle, nutrient
cycle, plant community dynamics and energy flow (see Table 2). The correlation analysis between
the functional indices and QM are indicated in Table 3. Similar to EHI, the SSI, WCI, CDI and EFI
were generally positively correlated to species richness and the percentage of total live vegetation
and negatively correlated with percentage of bare ground, at both ecoregions. Specifically, SSI, WCI
and EFI were highly correlated with the percentage of total live vegetation (R =0.69 to 0.85) and
bare ground (R =
0.74 to
0.93), indicating soil stability, water cycle and energy flow are potentially
eective indicators for ecological function.
Environments 2019,6, 67 8 of 18
Environments 2019, 6, x FOR PEER REVIEW 8 of 18
Figure 3. Linear regression for SG between EHI and QMs species richness, cover percentage of total
live vegetation, bare ground and litter.
Table 3. The correlation analysis between functional indexes (SSI, WCI, CDI and EFI) and quantifiable
measurements (QM) in Humid Magellan Steppe (HMS) and Subandean Grasslands (SG).
Species
Richness Litter Standing
Dead Cryptogams Ephemeral Total Live
Vegetation
Bare
Ground
_________________________________________SSI §__________________________________________
HMS
R 0.44 0.10 0.43 0.29 0.41 0.80 0.92
R2 0.19 0.01 0.19 0.09 0.17 0.64 0.84
P 0.04 0.67 0.04 0.18 0.05 <0.0001 <0.0001
SG
R 0.51 0.37 0.06 0.27 0.22 0.77 0.83
R2 0.26 0.13 0.003 0.07 0.05 0.59 0.70
P 0.02 0.11 0.82 0.25 0.36 <0.0001 <0.0001
_________________________________________WCI__________________________________________
HMS
Figure 3.
Linear regression for SG between EHI and QMs species richness, cover percentage of total
live vegetation, bare ground and litter.
Table 3.
The correlation analysis between functional indexes (SSI, WCI, CDI and EFI) and quantifiable
measurements (QM) in Humid Magellan Steppe (HMS) and Subandean Grasslands (SG).
Species
Richness Litter Standing
Dead Cryptogams Ephemeral Total Live
Vegetation
Bare
Ground
_________________________________________SSI §__________________________________________
HMS
R 0.44 0.10 0.43 0.29 0.41 0.80 0.92
R20.19 0.01 0.19 0.09 0.17 0.64 0.84
P0.04 0.67 0.04 0.18 0.05 <0.0001 <0.0001
SG
R 0.51 0.37 0.06 0.27 0.22 0.77 0.83
R20.26 0.13 0.003 0.07 0.05 0.59 0.70
P0.02 0.11 0.82 0.25 0.36 <0.0001 <0.0001
Environments 2019,6, 67 9 of 18
Table 3. Cont.
Species
Richness Litter Standing
Dead Cryptogams Ephemeral Total Live
Vegetation
Bare
Ground
_________________________________________WCI__________________________________________
HMS
R 0.53 0.16 0.43 0.28 0.42 0.84 0.93
R20.28 0.02 0.19 0.08 0.18 0.71 0.86
P0.01 0.49 0.05 0.21 0.05 <0.0001 <0.0001
SG
R 0.58 0.48 0.13 0.28 0.17 0.69 0.82
R20.34 0.23 0.02 0.08 0.03 0.47 0.68
P0.01 0.03 0.58 0.24 0.48 0.001 <0.0001
_________________________________________CDI__________________________________________
HMS
R 0.49 0.36 0.13 0.23 0.10 0.49 0.29
R20.24 0.13 0.02 0.05 0.01 0.24 0.08
P0.02 0.10 0.56 0.30 0.65 0.02 0.19
SG
R 0.66 0.37 0.06 0.52 0.11 0.45 0.54
R20.44 0.13 0.004 0.27 0.01 0.20 0.30
P0.002 0.11 0.79 0.02 0.64 0.05 0.01
_________________________________________EFI__________________________________________
HMS
R 0.68 0.25 0.34 0.06 0.29 0.76 0.74
R20.46 0.06 0.12 0.003 0.09 0.58 0.55
P0.001 0.27 0.12 0.81 0.19 <0.0001 <0.0001
SG
R 0.27 0.26 0.02 0.37 0.24 0.85 0.85
R20.07 0.07 0.0004 0.14 0.06 0.73 0.73
P0.25 0.27 0.93 0.11 0.31 <0.0001 <0.0001
§
Functional indexes: Soil Stability Index (SSI), Water Cycle Index (WCI), Plant Community Dynamics Index (CDI),
Nutrient Cycle Index (NCI), Energy Flow Index (EFI).
3.2. Carrying Capacity
Participating managers were surveyed on the overall carrying capacity of their managed landscapes.
The average carrying capacity was 123 (ranging from 23 to 503) and 35 (ranging from 0 to 84) sheep
animal days per hectare in HMS and SG, respectively (data not shown). The EHI was positively and
significantly correlated with carrying capacity (R =0.72, P=0.0003 for HMS and R =0.57, P=0.02 for
SG (Table 4).
Table 4.
Ecological health indicator (EHI) and quantifiable measurements (QM) correlated with carrying
capacity in Humid Magellan Steppe (HMS) and Subandean Grasslands (SG).
EHI Species
Richness Litter Total Live
Vegetation
Bare
Ground
HMS
R 0.72 0.62 0.63 0.54 0.37
R20.51 0.39 0.40 0.30 0.14
P0.0003 0.0025 0.0021 0.01 0.08
SG
R 0.57 0.40 0.41 0.48 0.36
R20.33 0.16 0.17 0.23 0.13
P0.02 0.11 0.10 0.05 0.12
Environments 2019,6, 67 10 of 18
4. Discussion
4.1. Ecological Health Index and Quantifiable Measurements
Species richness, an indicator of plant diversity, is a useful metric for landscape health as it can
influence ecosystem multifunctionality and stability [
35
39
]. The consistent correlation between species
richness in our pilot locations and EHI suggest that EHI could be an eective assessment of grazing
land ecological health. Vegetation cover and bare ground percentage is likewise an important ecological
indicator as greater bare ground results in increased runoand sediment loss [
25
]. In this study, mean
live vegetation percentage was positively correlated with EHI at both ecoregions (Figure 2; Figure 3).
Martin et al. studied the impacts of precipitation pattern on grazing lands and reported that decreased
rainfall can be detrimental to net primary productivity while increased precipitation variability may
have negative, none or positive eects [
11
]. We did detect numerically dierent vegetation cover and
plant species between HMS and SG and assess this was partly attributed to the increased precipitation
and generally milder growing conditions in HMS. Conversely to vegetation cover, the percentage of
bare ground was negatively correlated with EHI in both ecoregions.
An Australian rangeland study by Bartley et al. [
25
] reported a consistency in evaluating ecological
health by using vegetation cover percentage and the soil surface condition (SSC) index derived from
the LFA method [
24
]. The authors reported that both vegetation cover percentage and the SSC index
were highly correlated with infiltration rate. We oer that EHI can be used in a similar fashion to assess
water cycle function. Moreover, the correlation between EHI and QM further confirm the eectiveness
of EHI. Besides plant composition and bare ground percentage, litter amount is also an ecological health
indicator and the percentage of litter cover can be influenced by grazing management [
40
,
41
]. Litter
percentage had a low correlation with EHI only in SG (but not HMS). The small live plant percentage
and lower species richness in SG may lead to less root biomass and diversity when compared to other
regions with more favorable growing conditions. Since root residues and exudates determine the labile
C input, litter may be more important to sustain ecosystem function in drier areas such as SG and to a
lesser degree HMS, to provide C input and a food source for soil microbes [
42
]. Moreover, the litter
cover can protect soil from erosion, which is critical in regions such as SG with less vegetation cover
and less precipitation [
43
]. Conversely, HMS has relatively higher precipitation which favors litter
decomposition, so the litter amount is not necessarily correlated to ecosystem functions.
The absolute values of correlation coecients (R) between EHI and species richness, percentage
of total live vegetation and bare ground were between 0.55 and 0.72 (P<0.05). Similar to our study,
Kosmas et al.
reported a correlation (R =0.57) between the desertification risk index and soil organic
matter content from a land degradation study attributing the causes of this correlation to other factors
such as land management, climate conditions or soil characteristics [
14
]. Similarly, a Colorado study
indicated correlation coecients between qualitative indicators and other quantitative measures
between 0.31 and 0.69 [
44
]. Tongway and Hindley also analyzed the relationship between the
LFA-derived indexes and dierent QM and they indicated a high correlation between nutrient cycling
index and respiration (R >0.9) and between infiltration index and infiltration (R >0.8) [
24
]. Likewise,
the infiltration index and measured infiltration rate was highly correlated (R >0.9) in the study of
Bartley et al. [
25
]. The higher correlation detected from these studies, however, were between the
functional index and its related processes (nutrient cycling index vs. respiration, infiltration index vs.
infiltration, invasive plant indicator vs. invasive plant foliar cover, bare ground indicator vs. basal
gap percentage, etc.). Conversely, in our study, EHI is an integrated index encompassing soil stability,
water cycle, nutrient cycle, plant community dynamics and energy flow, aiming to detect the whole
ecological status of a grazing land ecosystem.
Environments 2019,6, 67 11 of 18
4.2. EHI and Carrying Capacity
At both ecological sites, carrying capacity was positively correlated with the percentage of total
live vegetation. Similarly, other studies also indicate that carrying capacity can be improved by
promoting plant production [
45
]. Conversely, lower plant productivity, which may result in or attribute
to a greater area of bare ground or erosion pavement, may be detrimental to carrying capacity. In each
location, bare ground was negatively correlated with carrying capacity. The consistency of EHI and
QM on their relationship with carrying capacity further suggest that EHI can be a useful method to
evaluate ecological function. Importantly, while a relationship between EHI and SG carrying capacity
is positive, in this brittle environment there could be risk for conflating a positive correlation with an
overstocked landscape. However, this would only be in a short-term situation and overtime, the two
metrics, if actually related, would become more correlated.
The relationship between plant diversity and ecosystem function is not very well understood
and more studies are needed to determine the impacts of species richness on animal production [
46
].
In this study, data indicate that the carrying capacity was positively correlated to species richness
in HMS (
P=0.0025
) suggesting a potential advantage of biodiversity in supporting greater animal
productivity with high vegetation cover.
Overall, the strongest correlations we detected were between EHI and carrying capacity. This
correlation would indicate that EHI can be a useful indicator in overall landscape productivity. So,
while EHI was not closely aligned with every indicator we monitored, from a holistic sense the
aggregated monitoring approach eectively aligned with a high indicator of overall land productivity.
Ultimately, land with a greater EHI could eectively sustain a greater carrying capacity with improved
ecosystem function.
5. Conclusions
The EHI is a monitoring protocol that utilizes tools from multiple land assessment approaches.
By measuring both EHI and QM and conducting correlation analysis between the two, our data
indicate that EHI increased with species richness, percentage of total live vegetation and decreased with
percentage of bare ground. However, we did not detect relationships between the Shannon Wiener
Index and EHI. While there were positive and significant relationships between EHI and carrying
capacity. Thus, EHI can be useful to inform decision makers about ecological attributes associated with
positive landscape function. Regular monitoring of such attributes leads to a greater understanding
of the relationship between management and ecosystem services in agricultural ecosystems and
provides information on how to improve subsequent management. We suggest that a combination of
frequent EHI monitoring with long-term QM, as proposed by Borrelli et al., can provide a cost-eective
assessment of ecosystem health in grazing lands [31].
Author Contributions:
Conceptualization, P.B., S.X., M.R.R., J.H. and J.R.; Methodology, P.B., S.X., M.R.R. and
J.R.; Validation, P.B. and S.X.; Formal Analysis, P.B., S.X., J.R. and M.R.R.; Investigation, P.B.; Resources, P.B.; Data
Curation, S.X. and P.B.; Writing-Original Draft Preparation, S.X.; Writing-Review & Editing, S.X., P.B., M.R.R., J.H.
and J.R.; Visualization, S.X., M.R.R. and J.R.; Supervision, P.B.; Project Administration, P.B. and J.R.
Funding: This research received no external funding.
Acknowledgments: The authors have no acknowledgments.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
The evaluation matrix for reference areas in HMS (Table A1) and SG (Table A2).
Environments 2019,6, 67 12 of 18
Table A1. Evaluation Matrix (Humid Magellan Steppe Descriptors).
Departure from Reference Sheet
Num. Atribute Process
Indicator Score N-S S-M M M-E E-T
1
Vegetation cover
% Vegetation
cover 10 to +10
Total perennial
vegetation cover exceeds
95%
Total perennial
vegetation between
90–95%
Total perennial
vegetation between
80–90%
Total perennial vegetation
between 70–80%
Total perennial vegetation less
than 70%
10 5 0 510
2Capping Surface soil
resistance 10 to +10
Loose soil or light
capping that breaks easily
with finger tip
Loose soil or light
capping that breaks easily
with finger tip
Loose soil or light
capping that breaks easily
with finger tip
Moderately hard capping
requires pressure to break
Very hard capping requires
metallic tool to break
10 5 0 510
3 Wind erosion
Active blowout/
deposition
processes 0 to 20
Not present Not present Slight soil movement Blowout/deposition areas cover
10–25% of the area
Blowout/deposition areas >25%
of the area
Active pedestals
Not present Not present Few active active
pedestals, hard to find Active pedestals 5–10 cm deep Pedestals abundant and active,
more than 10 cm deep
Total 0 0 0 10 20
4Water erosion
Active rills
0 to 20
Not present Not present Not present Laminar erosion or active rills
evident and well defined
Rill formation is severe and well
defined throughout the site
Active water
flows Not present Not present Not present Visible waterflows width <than
2 cm
Visible water flows, width >2 cm
Active gullies Not present Not present Not present Active gullies present, low
frequency Active gullies frequent
Total 0 0 0 10 20
5Litter
incorporation
Litter/soil
contact 10
More than 50% of the area
with low incorporation
20 to 50% of the area with
low incorporation Incorporation null Incorporation null Incorporation null
10 5 0 0 0
6Living
organisms
Evidence of
microfauna 10
Abundant presence of
dung beetles, aunts,
spiders and other species
Moderate presence of
dung beetles, aunts,
spiders and other species
Scarce presence of dung
beetles, aunts, spiders
and other species
Scarce presence of dung beetles,
aunts, spiders and other species
Scarce presence of dung beetles,
aunts, spiders and other species
10 5 0 0 0
7Dung
decomposition
Dung age
structure 10
Only fresh dung is
present. Fast cycling
Fresh and old dung
mixed
Most of dung patches are
more than 1 year old
(mummified)
Most of dung patches are more
than 1 year old (mummified)
Most of dung patches are more
than 1 year old (mummified)
10 5 0 0 0
8 Tussock
Tussock in good
condition
10 to 10
>30% 20–30% 10–20% <10% 0
Decadent
tussock <20% 20–30% 30–40% 40–50% >50%
Total 10 5 0 510
Environments 2019,6, 67 13 of 18
Table A1. Cont.
Departure from Reference Sheet
Num. Atribute Process
Indicator Score N-S S-M M M-E E-T
9 Decreasers Frequency 10
>10 plants/m21–10 plants/m2<1 plant/m2Decreaser species absent Decreaser species absent
10 5 0 0 0
10 Key species
Plants in good
condition
20 to 20
>50% 30–50% 10–30% <10% <10%
Decadent plants <10% 10–30% 30–50% 50–70% >70%
20 10 0 10 20
11 Shrubs
Plants in good
condition
10 to 10
>50% 30–50% 10–30% <10% Not observed
Decadent plants <10% 10–20% 20–30% 30–50% >50%
10 5 0 510
12 Invaders Abundance Not observed Not observed Not observed
Moderate presence of young
plants of Empetrum rubrum,
Azzorella or Hieracium pilosella
Abundant presence of young
plants of Empetrum rubrum,
Azzorella or Hieracium pilosella
000 10 20
13
Total production
% of Reference
area 10 to 10
More than 75% of
reference area 60–75% of reference area 50–60% of reference area 25–50% of reference area <25% of reference area
10 5 0 510
110 55 0 65 130
N-S Nil to slight
S-M Slight to moderate
M Moderate
M-E Moderate to extreme
E-T Extreme to total
FUNCTIONAL GROUPS
TUSSOCK
Tall, rough bunchgrasses—Provide structure, produce litter and have deep roots
Decreasers
Plants that disappear under continuous grazing. Usually broad leaved, more mesic grasses and native legumes
Key
Species Abundant, preferred species. Determine most of the high quality forage
Shrubs Woody plants that create niches and provide forage in winter time
Invaders (old term for contextually undesirable species). Plants that are emblematic of an undesired transition. Mostly exotic or unpalatable native woody plants
Environments 2019,6, 67 14 of 18
Table A2. Evaluation Matrix (Subandean Grassland Descriptions).
Departure from Reference Sheet
Num. Atribute Process
Indicator Score N-S S-M M M-E E-T
1Litter %Cover 0 to 10
Grade 3 litter or more,
more than 15% cover
Grade 3 Litter: 10–15%
cover
Grade 2 Litter: 1–10%
cover Grade 1 Litter: <1% cover Grade 1 Litter: <1% cover
10 5 0 0 0
2
Vegetation cover
% Vegetation
cover 10 to +10
Perennial Vegetation
cover exceeds 60% Bare
ground less than 20%
Perennial Vegetation
cover 55–60%. Bare
Ground 20–25%
Perennial Vegetation
cover 55–60%. Bare
Ground 25–35%
Perennial Vegetation cover
40–55%. Bare Ground 35–50%
Perennial Vegetation cover <40%.
Bare Ground >50%
10 5 0 510
3Capping Surface soil
resistance 10 to +10
Loose soil or light
capping that breaks easily
with finger tip
Loose soil or light
capping that breaks easily
with finger tip
Loose soil or light
capping that breaks easily
with finger tip
Moderately hard capping
requires pressure to break
Very hard capping requires
metallic tool to break
000 510
4Wind erosion
Active blowout/
deposition
processes
20 to 20
Not present (Grade 4) Grade 4 predominant,
less than 30% Grade 3 Grade 3 predominant Blowout/deposition areas cover
10–25% of the area
Blowout/deposition areas >25%
of the area (Grade 2)
Active pedestals
Not present (Grade 4) Not present (Grade 4)
Few active active
pedestals, hard to find
(Grade 3)
Active pedestals 5–10 cm deep
(Grade 2)
Pedestals abundant and active,
more than 10 cm deep
Total 20 10 0 10 20
5 Water erosion
Active rills
0 to 20
Not present (Grade 4) Not present (Grade 4) Not present (Grade 4) Laminar erosion or active rills
evident and well defined
Rill formation is severe and well
defined throughout the site
Active water
flows Not present (Grade 4) Not present (Grade 4) Not present (Grade 4) Visible waterflows width <than
2 cm (Grade 3)
Visible water flows, width >2
cm (Grade 2 and 1)
Active gullies Not present (Grade 4) Not present (Grade 4) Not present (Grade 4) Active gullies present, low
frequency Active gullies frequent
Total 0 0 0 10 20
6Biological crust Cover 0 to 10
>5% (Grade 3) Between 1–5% (Grade 2) <1% (Grade1) Not Present (Grade 0) Not Present (Grade 0)
10 5 0 0 0
7Litter
incorporation
Litter/soil
contact 0 to 10
More than 50% of the area
with low incorporation
20 to 50% of the area with
low incorporation Incorporation null Incorporation null Incorporation null
10 5 0 0 0
8Living
organisms
Evidence of
microfauna 10 to 10
Moderate presence of
dung beetles, aunts,
spiders and other species
Scarce presence of dung
beetles, aunts, spiders
and other species
Scarce presence of dung
beetles, aunts, spiders
and other species
Scarce presence of dung beetles,
aunts, spiders and other species
Scarce presence of dung beetles,
aunts, spiders and other species
5 0 0 0 0
9Dung
decomposition
Dung age
structure 0 to 10
Does not apply Does not apply Does not apply Does not apply Does not apply
0 0 0 0 0
Environments 2019,6, 67 15 of 18
Table A2. Cont.
Departure from Reference Sheet
Num. Atribute Process
Indicator Score N-S S-M M M-E E-T
10 Tussock
Tussock in good
condition
10 to 10
>40% 25–40% 10–25% <10% Not observed
Decadent
tussock <10% 10–25% 25–40% 40–60% >60%
Total 10 5 0 510
11 Decreasers Frequency 0 to 10
Very abundant >10
plants/m21–10 plants per m2less than 1 plant/m2Decreasers are absent Decreasers are absent
10 5 0 0 0
12 Key species
Plants in good
condition
20 to 20
>40% 25–40% 10–25% <10% Not observed
Decadent plants <10% 10–25% 25–40% 40–60% >60%
20 10 0 10 20
13 Shrubs
Plants in good
condition
10 to 10
>50% 30–50% 10–30% <10% Not observed
Decadent plants <10% 10–20% 20–30% 30–50% >50%
10 5 0 510
14 Invaders Abundance 0 to 20
Not observed Not observed Not observed
Rare to moderate presence of
young plants of Stipa sp,
Nassauvia and Acaena
Frequent presence of young
plants of Stipa sp, Nassauvia and
Acaena
000 10 20
15
Total production
% of Reference
area
More than 75% of
Reference Area
60–75% of Reference Area 50–60% of Reference Area
25–50% of Reference Area <25% of Reference Area
10 5 0 510
125 60 0 65 130
Environments 2019,6, 67 16 of 18
References
1.
Follett, R.F.; Reed, D.A. Soil carbon sequestration in grazing lands: Societal benefits and policy implications.
Rangel. Ecol. Manag. 2010,63, 4–15. [CrossRef]
2.
Thornton, P.K. Livestock production: Recent trends, future prospects. Philos. Trans. R. Soc. Lond. B Biol. Sci.
2010,365, 2853–2867. [CrossRef] [PubMed]
3.
Oba, G.; Vetaas, O.R.; Stenseth, N.C. Relationships between biomass and plant species richness in arid-zone
grazinglands. J. Appl. Ecol. 2001,38, 836–845. [CrossRef]
4.
Parkpian, P.; Leong, S.T.; Laortanakul, P.; Thunthaisong, N. Regional monitoring of lead and cadmium
contamination in a tropical grazingland site, Thailand. Environ. Monit. Assess.
2003
,85, 157–173. [CrossRef]
[PubMed]
5.
Veblen, K.E.; Pyke, D.A.; Aldridge, C.L.; Casazza, M.L.; Assal, T.J.; Farinha, M.A. Monitoring of livestock
grazing eects on Bureau of Land Management land. Rangel. Ecol. Manag. 2014,67, 68–77. [CrossRef]
6.
Slimani, H.; Aidoud, A.; Roze, F. 30 Years of protection and monitoring of a steppic rangeland undergoing
desertification. J. Arid Environ. 2010,74, 685–691. [CrossRef]
7.
USDA NRCS. Inventorying and Monitoring Grazing Land Resources. In National Range and Pasture
Handbook; USDA: Washington, DC, USA, 2006. Available online: https://directives.sc.egov.usda.gov/
OpenNonWebContent.aspx?content=17739.wba (accessed on 27 June 2018).
8.
Pickup, G.; Bastin, G.N.; Chewings, V.H. Remote-sensing-based condition assessment for nonequilibrium
rangelands under large-scale commercial grazing. Ecol. Appl. 1994,4, 497–517. [CrossRef]
9.
Hill, J.; Hostert, P.; Tsiourlis, G.; Kasapidis, P.; Udelhoven, T.; Diemer, C. Monitoring 20 years of increased
grazing impact on the Greek island of Crete with earth observation satellites. J. Arid Environ.
1998
,39,
165–178. [CrossRef]
10.
Del Barrio, G.; Puigdefabregas, J.; Sanjuan, M.E.; Stellmes, M.; Ruiz, A. Assessment and monitoring of land
condition in the Iberian Peninsula, 1989–2000. Remote Sens. Environ. 2010,114, 1817–1832. [CrossRef]
11.
Martin, R.; Müller, B.; Linstädter, A.; Frank, K. How much climate change can pastoral livelihoods tolerate?
Modelling rangeland use and evaluating risk. Glob. Environ. Chang. 2014,24, 183–192. [CrossRef]
12.
Gessesse, B.; Bewket, W.; Bräuning, A. Model-based characterization and monitoring of runoand soil
erosion in response to land use/land cover changes in the Modjo watershed, Ethiopia. Land Degrad. Dev.
2015,26, 711–724. [CrossRef]
13.
Henderson, B.B.; Gerber, P.J.; Hilinski, T.E.; Falcucci, A.; Ojima, D.S.; Salvatore, M.; Conant, R.T. Greenhouse
gas mitigation potential of the world’s grazinglands: Modeling soil carbon and nitrogen fluxes of mitigation
practices. Agric. Ecosyst. Environ. 2015,207, 91–100. [CrossRef]
14.
Kosmas, C.; Kairis, O.; Karavitis, C.; Ritsema, C.; Salvati, L.; Acikalin, S.; Alcal
á
, M.; Alfama, P.; Atlhopheng, J.;
Barrera, J.; et al. Evaluation and selection of indicators for land degradation and desertification monitoring:
Methodological approach. Environ. Manag. 2014,54, 951–970. [CrossRef] [PubMed]
15.
ˇ
Cuˇcek, L.; Klemeš, J.J.; Kravanja, Z. A review of footprint analysis tools for monitoring impacts on
sustainability. J. Clean. Prod. 2012,34, 9–20. [CrossRef]
16.
Pyke, D.A.; Herrick, J.E.; Shaver, P.; Pellant, M. Rangeland health attributes and indicators for qualitative
assessment. J. Range Manag. 2002,55, 584–597. [CrossRef]
17.
Pellant, M.; Shaver, P.; Pyke, D.; Herrick, J. Interpreting Indicators of Rangeland Health, Version 4; Technical
Reference 1734-6; US Department of Interior, Bureau of Land Management, National Science and Technology
Center: Denver, CO, USA, 2005; 122p.
18.
Herrick, J.E.; Bestelmeyer, B.T.; Archer, S.; Tugel, A.J.; Brown, J.R. An integrated framework for science-based
arid land management. J. Arid Environ. 2006,65, 319–335. [CrossRef]
19.
Schwilch, G.; Bestelmeyer, B.; Bunning, S.; Critchley, W.; Herrick, J.; Kellner, K.; Liniger, H.P.; Nachtergaele, F.;
Ritsema, C.J.; Schuster, B.; et al. Experiences in monitoring and assessment of sustainable land management.
Land Degrad. Dev. 2011,22, 214–225. [CrossRef]
20.
Toevs, G.R.; Karl, J.W.; Taylor, J.J.; Spurrier, C.S.; Karl, M.S.; Bobo, M.R.; Herrick, J.E. Consistent indicators
and methods and a scalable sample design to meet assessment, inventory, and monitoring information needs
across scales. Rangelands 2011,33, 14–20. [CrossRef]
21.
Mitchell, J.E. Criteria and Indicators for Sustainable Rangeland Management; Cooperative Extension Service
Publication SM-56; University of Wyoming: Laramie, WY, USA, 2010; p. 227.
Environments 2019,6, 67 17 of 18
22.
Ludwig, J.A.; Bastin, G.N.; Eager, R.W.; Karfs, R.; Ketner, P.; Pearce, G. Monitoring Australian rangeland sites
using landscape function indicators and ground-and remote-based techniques. Environ. Monit. Assess.
2000
,
64, 167–178. [CrossRef]
23.
Borrelli, P.; Oliva, G. Evaluaci
ó
n de Pastizales. Cap
í
tulo 6. In Ganader
í
a Ovina Sustentable en la Patagonia Austral;
Borrelli, P., Oliva, G., Eds.; Centro Regional Patagonia Sur INTA: R
í
o Gallegos: Santa Cruz, Argentina, 2001.
24. Tongway, D.J.; Hindley, N.L. Landscape Function Analysis: Procedures for Monitoring and Assessing Landscapes
with Special Reference to Minesite and Rangelands; CSIRO: Canberra, Australia, 2004; 80p.
25.
Bartley, R.; Roth, C.H.; Ludwig, J.; McJannet, D.; Liedlo, A.; Corfield, J.; Hawdon, A.; Abbott, B. Runoand
erosion from Australia’s tropical semi-arid rangelands: Influence of ground cover for diering space and
time scales. Hydrol. Process. 2006,20, 3317–3333. [CrossRef]
26.
Read, Z.J.; King, H.P.; Tongway, D.J.; Ogilvy, S.; Greene, R.S.B.; Hand, G. Landscape function analysis to
assess soil processes on farms following ecological restoration and changes in grazing management. Eur. J.
Soil Sci. 2016,67, 409–420. [CrossRef]
27.
Van der Walt, L.; Cilliers, S.S.; Kellner, K.; Tongway, D.; Van Rensburg, L. Landscape functionality of plant
communities in the Impala Platinum mining area, Rustenburg. J. Environ. Manag.
2012
,113, 103–116.
[CrossRef] [PubMed]
28.
Canfield, R.H. Application of the line interception method in sampling range vegetation. J. For.
1941
,39,
388–394.
29.
Tothill, J.C.; Hargreaves, J.N.G.; Jones, R.M.; McDonald, C.K. BOTANAL—A comprehensive sampling and
computing procedure for estimating pasture yield and composition. 1. Field sampling. Trop. Agron. Tech. Memo.
1992
,78, 1–24. Available online: https://www.researchgate.net/profile/Cam_Mcdonald/publication/303169091_
BOTANAL_A_comprehensive_sampling_procedure_for_estimating_pasture_yield_and_composition_I_Field_
sampling/links/5a3a12f4458515889d2bd450/BOTANAL-A-comprehensive-sampling-procedure-for-estimating-
pasture-yield-and-composition-I-Field-sampling.pdf (accessed on 30 May 2018).
30.
Buckland, S.T.; Anderson, D.R.; Burnham, K.P.; Laake, J.L.; Borchers, D.L.; Thomas, L. Introduction to Distance
Sampling Estimating Abundance of Biological Populations; Oxford University Press: New York, NY, USA, 2001.
31.
Borrelli, P.F.; Boggio, P.; Sturzenbaum, M.; Paramidani, R.; Heinken, C.; Pague, M. Stevens and A. Nogu
é
s.
Grassland Regeneration and Sustainable Standard (GRASS); The Nature Conservancy: Arlington County, VA,
USA, 2012; p. 109. Available online: http://www.fao.org/fileadmin/user_upload/nr/sustainability_pathways/
docs/GRASS%20english.pdf (accessed on 20 June 2018).
32.
Oliva, G.; Gait
á
n, J.; Bran, D.; Nakamatsu, V.; Salomone, J.; Buono, G.; Escobar, J.; Frank, F.;
Ferrante, D.; Humano, G.; et al. Monitoreo Ambiental Para Regiones
Á
ridas y Semi
á
ridas. Available
online: http://gefpatagonia.ambiente.gob.ar/archivos/web/MSEAySACDP/file/MARAS_Manual_mayo_2010.
pdf (accessed on 15 May 2018).
33.
Halloy, S.; Ibañez, M.; Yager, K. Point and flexible area sampling for rapid inventories of biodiversity status.
Ecología en Bolivia 2011,46, 46–56.
34.
Asbjornsen, H.; Hernandez-Santana, V.; Liebman, M.; Bayala, J.; Chen, J.; Helmers, M.; Ong, C.K.; Schulte, L.A.
Targeting perennial vegetation in agricultural landscapes for enhancing ecosystem services. Renew. Agric.
Food Syst. 2014,29, 101–125. [CrossRef]
35.
Symstad, A.J.; Jonas, J.L. Incorporating biodiversity into rangeland health: Plant species richness and
diversity in Great Plains grasslands. Rangel. Ecol. Manag. 2011,64, 555–572. [CrossRef]
36.
Zavaleta, E.S.; Pasari, J.R.; Hulvey, K.B.; Tilman, G.D. Sustaining multiple ecosystem functions in grassland
communities requires higher biodiversity. Proc. Natl. Acad. Sci. USA 2010,107, 1443–1446. [CrossRef]
37.
Allan, E.; Manning, P.; Alt, F.; Binkenstein, J.; Blaser, S.; Blüthgen, N.; Böhm, S.; Grassein, F.; Hölzel, N.;
Klaus, V.H.; et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and
changes to functional composition. Ecol. Lett. 2015,18, 834–843. [CrossRef]
38.
Hallett, L.M.; Stein, C.; Suding, K.N. Functional diversity increases ecological stability in a grazed grassland.
Oecologia 2017,183, 831–840. [CrossRef]
39.
Papanastasis, V.P.; Bautista, S.; Chouvardas, D.; Mantzanas, K.; Papadimitriou, M.; Mayor, A.G.; Koukioumi, P.;
Papaioannou, A.; Vallejo, R.V. Comparative assessment of goods and services provided by grazing regulation
and reforestation in degraded Mediterranean rangelands. Land Degrad. Dev.
2015
,28, 1178–1187. [CrossRef]
40.
Toledo, D.; Sanderson, M.; Herrick, J.; Goslee, S. An integrated approach to grazingland ecological assessments
and management interpretations. J. Soil Water Conserv. 2014,69, 110A–114A. [CrossRef]
Environments 2019,6, 67 18 of 18
41.
Weber, K.T.; Gokhale, B.S. Eect of grazing on soil-water content in semiarid rangelands of southeast Idaho.
J. Arid Environ. 2011,75, 464–470. [CrossRef]
42.
Shamoot, S.; McDonald, L.; Bartholomew, W.V. Rhizo-deposition of organic debris in soil. Soil Sci. Soc. Am. J.
1968,32, 817–820. [CrossRef]
43.
Descroix, L.; Viramontes, D.; Vauclin, M.; Barrios, J.G.; Esteves, M. Influence of soil surface features and
vegetation on runoand erosion in the Western Sierra Madre (Durango, Northwest Mexico). Catena
2001
,43,
115–135. [CrossRef]
44.
Kachergis, E.; Rocca, M.E.; Fernandez-Gimenez, M.E. Indicators of ecosystem function identify alternate
states in the sagebrush steppe. Ecol. Appl. 2011,21, 2781–2792. [CrossRef]
45.
Waldron, B.L.; Greenhalgh, L.K.; ZoBell, D.R.; Olson, K.C.; Davenport, B.W.; Palmer, M.D. Forage Kochia
Increases Nutritional Value, Carrying Capacity, and Livestock Performance on Semiarid Rangelands.
Forage Grazinglands 2011,9. [CrossRef]
46.
Sanderson, M.A.; Skinner, R.H.; Barker, D.J.; Edwards, G.R.; Tracy, B.F.; Wedin, D.A. Plant species diversity
and management of temperate forage and grazing land ecosystems. Crop Sci.
2004
,44, 1132–1144. [CrossRef]
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