Fragmentation Effects on Soil Aggregate Stability in a Patchy Arid Grassland

Bestelmeyer, Brandon T.; Ward, Judy P.; Tugel, Arlene J.

doi:10.2111/05-180r1.1

Cited by 19 publications

(3 citation statements)

References 46 publications

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“…Coordinate errors can originate in the submitted manuscript, or can be introduced in the formatting and typesetting process. For example, Bestelmeyer et al (2006) reported their study area location correctly using UTM coordinates, but the coordinate values were erroneously reformatted (without applying a geographic transformation) into degrees, minutes, and seconds by the publisher, resulting in the coordinate describing a location in the middle of the Mediterranean Sea that should have been in the southwestern deserts of the United States. This error is known only by knowledge of the study area and personal communication with the authors.…”

Section: Discussionmentioning

confidence: 99%

Mining location information from life- and earth-sciences studies to facilitate knowledge discovery

Karl

2018

Journal of Librarianship and Information Science

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Location information in published studies represents an untapped resource for literature discovery, applicable to a range of domains. The ability to easily discover scientific articles from specific places, nearby locales, or similar (but geographically separate) areas worldwide is important for advancing science and addressing global sustainability challenges. However, the thematic and not geographic nature of current search tools makes location-based searches challenging and inefficient. Manually geolocating studies is labor intensive, and place-name recognition algorithms have performed poorly due to prevalence of irrelevant place names in scientific articles. These challenges have hindered past efforts to create map-based literature search tools. Thus, automated approaches are needed to sustain article georeferencing efforts. Common pattern-matching algorithms (parsers) can be used to identify and extract geographic coordinates from the text of published articles. Pattern-matching algorithms (geoparsers) were developed using regular expressions and lexical parsing and tested their performance against sets of full-text articles from multiple journals that were manually scanned for coordinates. Both geoparsers performed well at recognizing and extracting coordinates from articles with accuracy ranging from 85.1% to 100%, and the lexical geoparser performing marginally better. Omission errors (i.e. missed coordinates) were 0% to 14.9% for the regular expression geoparser and 0% to 10.3% for the lexical geoparser. Only a single commission error (i.e. erroneous coordinate) was encountered with the lexical geoparser. The ability to automatically identify and extract location information from published studies opens new possibilities for transforming scientific literature discovery and supporting novel research.

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Section: Discussionmentioning

confidence: 99%

Mining location information from life- and earth-sciences studies to facilitate knowledge discovery

Karl

2018

Journal of Librarianship and Information Science

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“…Heterogeneity in biological, chemical or physical characteristics of soils and landforms produce patchiness in vegetation, degradation and recovery processes, and sensitivity to disturbance in many landscapes (Cox and Gakahu 1987;Friedel et al 1993;McAuliffe 1994;Stroh et al 2001;Ludwig et al 2005;Bestelmeyer et al 2006b). For example, Bestelmeyer et al (2006c) showed that different patch types within a Chihuahuan Desert grassland in North America would be likely to respond to degradation processes at different rates, mediated by differences in soil aggregate stability associated with bare patch size and connectivity. Numerous other landscape features and processes could provide a basis for selecting indicator patches (e.g.…”

Section: Discussionmentioning

confidence: 99%

Indicator patches: exploiting spatial heterogeneity to improve monitoring systems

et al. 2009

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When choosing which environmental attributes to monitor in assessing disturbance, it is important to consider not only which metric will provide the most sensitive indicator of disturbance, but also the spatial considerations of where in the landscape that metric will be most responsive to change. Degradation in landscapes is often unevenly expressed because: (i) disturbance is spatially localised, (ii) landscape elements differ in their sensitivity to disturbance, and (iii) degradation following localised disturbance is spatially contagious. The spatial heterogeneity of degradation has proven to be a key obstacle to rangeland monitoring (e.g. where the initiating processes of broad-scale degradation are concentrated in landscape locations that are not detected by surveys) but can also provide opportunities to focus monitoring efforts. We propose that the effectiveness of monitoring could be enhanced by identifying and selectively monitoring ‘indicator patches’, i.e. specific landscape locations that provide the most management-relevant and timely information about the consequences of a monitored disturbance. We tested and demonstrated the utility of the ‘indicator patch’ concept in the rangelands of the Succulent Karoo in southern Africa. We contrasted the grazing response of dominant ‘representative’ vegetation, with responses of interspersed patches of distinct vegetation associated with zoogenic mounds. Since mound vegetation is more palatable and preferentially grazed by sheep, we tested whether mounds could serve as ‘indicator patches’ in providing a sensitive measure of grazing disturbance. Percentage canopy cover measurements in dominant off-mound vegetation provided a poor indicator of grazing disturbance (although more intensive plant size measurements did reveal grazing impacts on plant population dynamics). In contrast, vegetation on mounds displayed patterns of changes in species abundances that were easier to detect and useful for interpreting and quantifying the effects of grazing. Mound vegetation could, therefore, be used as ‘indicator patches’ and targeted for exclusive sampling as a sensitive method for monitoring rangeland condition and detecting early warnings of vegetation change. This approach could be widely employed to better harness the extensive knowledge base regarding the patchy, spatially localised nature of degradation-initiating processes in numerous other landscapes. Routinely incorporating this understanding into the design of monitoring programs could improve the effectiveness of sampling effort, allow detection of more subtle trends (changes), and provide earlier warning of impending degradation so remedial action can be taken before degradation becomes severe and widespread.

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“…While STMs that are defined purely by structural attributes can provide valuable information about vegetational change for a site, they do not describe the underlying structuralfunctional feedbacks that contribute to resilience. State-and-transition models that include such descriptions of ecological function can provide better guidance than structural descriptions alone for managers designing conservation and restoration prescriptions that enhance ecosystem resilience (Stringham et al 2003, Bestelmeyer et al 2006, Briske et al 2008, Lopez et al 2013.…”

Section: Introductionmentioning

confidence: 99%

Revision of a state‐and‐transition model to include descriptions of state functional attributes

et al. 2018

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Abstract. Successful conservation of ecosystems and ecosystem services requires understanding the structural-functional relationships underlying resilience to disturbance pressures. State-and-transition models (STMs) are box-and-arrow diagrams conceptualizing system resilience, but many STMs lack functional descriptions. For a cattle-grazed sagebrush steppe ecosystem in northwestern Colorado, we aimed to update an existing STM by incorporating functional descriptions of the states to inform future hypotheses. In this STM, a diverse set of native perennial understory plants define a Diverse state, but long-term exclusion of disturbances to sagebrush results in a shift to a Depauperate state with increased bare ground interspaces and sagebrush dominance. We hypothesized that herbaceous species were inferior competitors to sagebrush over the long term for soil nitrogen. To examine evidence for this hypothesis, develop functional descriptions of the Diverse and Depauperate states, and identify more specific hypotheses for future studies, we measured the following: the mass, C:N, and distribution of the litter layer; nitrogen mineralization rates; nitrate pools; and soil attributes on plots classified as Diverse (n = 5) or Depauperate (n = 5). Plots in the Depauperate state had~20% higher soil bulk density, half the clay and soil moisture concentrations, and 30% lower percent soil nitrogen compared to Diverse state plots; litter layer patchiness was nearly three times greater on the Depauperate plots than on the Diverse plots, but there was no significant difference between states in mean litter layer mass or C:N. We detected no significant differences between states in nitrogen mineralization rates or nitrate pools. This may be due to our sampling design, which examined nitrogen transformation at the plot level rather than at microsites within the plots, or it may be that another resource such as soil moisture is more limiting than nitrogen. Differences in soil texture between plots in the Diverse and Depauperate states may indirectly influence the competitive interactions between sagebrush and native herbaceous understory by controlling available soil resources. These results suggest that the vulnerability of sagebrush steppe ecosystems to state changes may be dependent on soil texture, which is highly variable across this broadly distributed ecosystem. Based on these insights, we present a revised STM and propose future work.

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Fragmentation Effects on Soil Aggregate Stability in a Patchy Arid Grassland

Cited by 19 publications

References 46 publications

Mining location information from life- and earth-sciences studies to facilitate knowledge discovery

Mining location information from life- and earth-sciences studies to facilitate knowledge discovery

Indicator patches: exploiting spatial heterogeneity to improve monitoring systems

Revision of a state‐and‐transition model to include descriptions of state functional attributes

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