Landscape function analysis: a system for monitoring rangeland function

Tongway, David J.; Hindley, Norman

doi:10.2989/10220110409485841

Cited by 101 publications

(105 citation statements)

References 0 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…Their first model determined (R 2 ¼ 0.67) that rangeland production was primarily driven by (1) soil profile effective thickness and (2) total nitrogen percentage-soil stability was not among the best predictors despite it being positively correlated with yield. We suspect it is possible to determine the best minimum set of predictor variables for the Landscape Function Analysis monitoring system which has validated its composite metrics of ecosystem functioning (e.g., Tongway and Hindley 2004a). An analysis of existing indicator data would be extremely useful and help quantify the relative importance of individual indicator variables.…”

Section: Discussionmentioning

confidence: 99%

Soil aggregate stability was an uncertain predictor of ecosystem functioning in a temperate and semiarid grassland

et al. 2015

View full text Add to dashboard Cite

Abstract. We estimate rangeland managers assessing ecosystem health have measured soil stability .800,000 times. Our aim was to use quantitative data from a site in the Northern Great Plains, USA and a semi-quantitative literature search to demonstrate the robustness of soil stability as an indicator of ecosystem functioning. Empirical data included measurements of plant and soil properties along a local livestock grazing gradient to determine whether soil stability (e.g., % water-stable aggregates) explained primary productivity and soil water transport for a mixed-grass prairie site in the Northern Great Plains. We measured: annual net primary productivity (ANPP), elevation, % soil moisture, measures of soil stability, and soil water transport (field-saturated infiltrability and sorptivity) across points spanning a local gradient in livestock grazing intensity (none vs. light to moderate stocking rates; mean distance separating points ¼ 39.9 m [range ¼ 5.2-71.3 m]). Across the sampled gradient, variation in ANPP was best explained by a model with field-saturated infiltrability and % soil moisture. Infiltrability explained slightly more of the variation. We then determined that moderate amounts of variation in infiltrability were explained by ANPP, % soil moisture, and % water-stable aggregates. We determined that most of this variation was explained by ANPP and then soil moisture. Our empirical findings indicate that plant production was correlated with infiltration though we could not determine whether variation in plant production was caused by variation in infiltration or vice versa. We generally failed to show that soil stability (e.g., % waterstable aggregates) was a useful predictor of primary productivity and soil water transport. Our semiquantitative literature review also indicated that soil stability was not a consistent predictor of either plant production or infiltration. The varying evidence reported here on whether soil stability is a predictor of ecosystem function illustrates the difficulty in identifying an indicator of ecosystem health that (1) is a predictor of ecosystem function across grassland types, (2) is sensitive to rangeland management, and (3) can be easily implemented by non-experts.

show abstract

Section: Discussionmentioning

confidence: 99%

Soil aggregate stability was an uncertain predictor of ecosystem functioning in a temperate and semiarid grassland

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Biocrust re-establishment monitored over time will provide indicative measurements and information about soil function. This data may be representative of the key milestones relating to stability, nutrient cycling and infiltration (see Tongway and Hindley, 2004 6. Cyanobacteria with crust building and nutrient augmentation attributes which could lend themselves to use as inoculum for facilitative restoration were: Nostoc, Porphyrosiphon, Scytonema and Symploca.…”

Section: Microbial Biobankingmentioning

confidence: 99%

“…The important parameters include: (a) photosynthetic yield 10 via non-destructive field tests that verify biocrust development by microbes that fix CO2; (b) biocrust compressive strength via non-destructive field tests (using a penetrometer) that establish the relative strength of the re-established biocrust; (c) cyanobacterial cover: non-destructive field tests that establish the regrowth of surface dwelling species; and, (d) cyanobacterial chlorophyll; destructive tests that determines the chlorophyll concentration of the cyanobacterial component of the biocrust including sub-surface species. At J-A once Type 1-2 biocrusts have re-established temporal biocrust monitoring could be 15 incorporated into the Landscape Function Analysis program (Tongway and Hindley, 2004) that includes a measure of biocrusts in its raw indicator set. Type 1-2 biocrusts would be indicative of a stable early successional cyanobacterial crust that would provide important contributions to the soil ecosystem including microorganism diversity, stabilisation, carbon and nitrogen cycling and soil surface protection.…”

Section: Microbial Biobankingmentioning

confidence: 99%

“…Soil disturbance results in a loss of resources from arid ecosystems and often has long lasting effects on soil stability, nutrient cycling and surface hydrology (Bowker, 2007;Tongway and Hindley, 2004). Restoration of ecosystem function post disturbance requires comprehension of the dynamic functional status of the landscape prior to disturbance (Tongway and Ludwig, 1996), as well as an understanding of the net accumulative effects of disturbance on the components of that system.…”

Section: Introductionmentioning

confidence: 99%

“…Research into biocrust disturbance and recovery post-mining is rare (see Fischer et al, 2014;Lukešová, 2001;Mazor et al, 1996;Setyawan et al, 2016), yet there is a real need for focus on practical approaches that contribute to the restoration of soil 10 function and measure relevant aspects of success through soil microbial communities (Harris, 2003;Tongway and Hindley, 2004). Mine rehabilitation is a complex process that involves many levels of understanding of difficult issues relating to ecosystem function where the removal or burial of the bioactive soils can have knock-on effects for rehabilitation efforts such as native seedling establishment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Microbial Biobanking Cyanobacteria-rich topsoil facilitates mine rehabilitation

Williams¹,

Schneemilch²,

Chilton³

et al. 2017

Preprint

View full text Add to dashboard Cite

10Mining rehabilitation requires key solutions to complex issues relating to ecosystem function. In arid landscapes, the removal or disturbance of topsoil incorporating soil microbial communities can result in a shift in ecosystem function. Soil surfaces in arid regions are protected by biocrusts that regulate soil moisture, sequester carbon and fix significant quantities of atmospheric nitrogen. Cyanobacteria often dominate these bioactive surfaces and work as ecosystem engineers in that they are in sufficiently large quantities they initiate biocrust establishment and facilitate soil surface stabilisation. Cyanobacterial 15 exopolymeric secretions form cohesive and protective layers at the soil surface that minimise wind erosion. This research encompassed soil microbial community profiling (using a polyphasic approach) with a focus on 'biobanking' topsoil for rehabilitation purposes. The research was in collaboration with Iluka Resources at Jacinth-Ambrosia (J-A) mineral sand mine located in a semi-arid chenopod shrubland in southern Australia. At J-A diverse biocrusts included a significant representation of cyanobacteria, lichens and mosses that inhabited nearly half of all soil surfaces. Cyanobacterial community structure at J-A 20 was comprised of a variety of species having a range of attributes that contributed to their resilience and survival in an arid environment. Stockpiling from shallow scrapings and storage at low profiles appeared beneficial in microbial biobanking cyanobacterial inoculum that would facilitate recovery over time. These studies have provided information for the establishment of a monitoring program that assesses the re-establishment of biocrusts following mining. Following soil stockpiling that occurred during the mining process, cyanobacterial taxa recovered at different rates. Cyanobacterial strategies 25 central to survival include exopolymeric production, spectral adaptation, nitrogen fixation and motility. Biocrust reestablishment during mining rehabilitation relies on the role of cyanobacteria as a means of early soil stabilisation. Provided there is adequate cyanobacterial inoculum in the topsoil stockpiles their growth and the subsequent crust formation should take place largely unassisted. Ongoing monitoring of biocrust recovery is important as it provides an effective means of measuring important soil restoration processes. 30Biogeosciences Discuss., https://doi

show abstract

Hydrological thresholds of soil surface properties identified using conditional inference tree analysis

Tighe

Muñoz-Robles

Reid

et al. 2012

Earth Surf Processes Landf

View full text Add to dashboard Cite

There has been limited success in determining critical thresholds of ground cover or soil characteristics that relate to significant changes in runoff or sediment production at the microscale (<1 m2), particularly in semi‐arid systems where management of ground cover is critical. Despite this lack of quantified thresholds, there is an increasing research focus on the two‐phase mosaic of vegetation patches and inter‐patches in semi‐arid systems. In order to quantify ground cover and soil related thresholds for runoff and sediment production, we used a data mining technique known as conditional inference tree analysis to determine statistically significant values of a range of measured variables that predicted average runoff, peak runoff, sediment concentration and sediment production at the microscale. On Chromic Luvisols across a range of vegetation states in semi‐arid south‐eastern Australia, large changes in runoff and sediment production were related to a hierarchy of different variables and thresholds, but the percentage of bare soil played a primary role in predicting runoff and sediment production in most instances. The identified thresholds match well with previous thresholds found in semi‐arid and temperate regions (including the approximate values of 30%, 50% and 70% total ground cover). The analysis presented here identified the critical role of soil surface roughness, particularly where total ground cover is sparse. The analysis also provided evidence that a two‐phase mosaic of patches and inter‐patches identified via rapid visual assessment could be further delineated into distinct groups of hydrological response, or a multi‐phase rather than a two‐phase system. The approach used here may aid in assessing scale‐dependent responses and address data non‐linearity in studies of semi‐arid hydrology. Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

Landscape function analysis: a system for monitoring rangeland function

Cited by 101 publications

References 0 publications

Soil aggregate stability was an uncertain predictor of ecosystem functioning in a temperate and semiarid grassland

Soil aggregate stability was an uncertain predictor of ecosystem functioning in a temperate and semiarid grassland

Microbial Biobanking Cyanobacteria-rich topsoil facilitates mine rehabilitation

Hydrological thresholds of soil surface properties identified using conditional inference tree analysis

Contact Info

Product

Resources

About