Landscape Influences on Stream Habitats and Biological Assemblages

2006 ◽  
Keyword(s):  
Landscape Scale ◽  
Riparian Zones ◽  
Total Abundance ◽  
Stream Channels ◽  
Urban Centers ◽  
Explanatory Variables ◽  
Agricultural Areas ◽  
Abundance And Distribution ◽  
Shallow Stream ◽  
Agricultural Regions

<em>Abstract.</em>—Wood is an important component of small to medium-sized streams in forested regions, but has been poorly studied in agricultural areas. Our goals were to (1) characterize the abundance, size, and distribution of wood in low-gradient streams in two agricultural regions, (2) quantify the influence of reach- and landscape-scale factors on the abundance and distribution of wood in these streams, and (3) compare trends across two study areas. Wood abundance was quantified in stream reaches in two diverse agricultural regions of the Midwestern United States: central Michigan and southeastern Minnesota. Wood abundance was quantified in 71 stream reaches, and an array of channel, riparian zone, and landscape features were characterized. Multiple regressions were conducted to predict abundance from those explanatory variables. We found that large wood was relatively scarce in these low-gradient streams compared to low-gradient streams in forested regions. Mean log size was greater, but total abundance was lower in Minnesota than Michigan. In Minnesota, greatest wood abundance and greatest extent of accumulations were predicted in wide, shallow stream channels with high substrate heterogeneity and woody riparian vegetation overhanging the channel. Models were dominated by reach-scale variables. In Michigan, largest densities of wood and accumulations were associated with catchments in hilly regions containing urban centers, with low soil water capacity, wide, shallow stream channels, low coarse particular organic matter standing stocks, and woody riparian zones. Models contained both reach- and landscape-scale variables. Difference in the extent of agricultural and forest land use/cover between Michigan and Minnesota may explain the differences in the models predicting wood variables. Patterns in wood abundance and distribution in these Midwestern streams differ from those observed in high gradient regions, and in low-gradient streams within forested regions. This has important implications for ecosystem processes and management of headwater streams in agricultural regions.

Nitrogen retention effect of riparian zones in agricultural areas: A meta-analysis

2021 ◽  
pp. 128143
Author(s):  
Chunjian Lyu ◽  
Xiaojie Li ◽  
Peng Yuan ◽  
Yonghui Song ◽  
Hongjie Gao ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Nitrogen Retention ◽  
Riparian Zones ◽  
Agricultural Areas

scholarly journals The importance of historical maps for man-made pond research: From the past extent of ponds to issues of the present landscape. A case study from the Czech Republic

2021 ◽  
Vol 29 (3) ◽  
pp. 184-201
Author(s):  
Jindřich Frajer ◽  
Jana Kremlová ◽  
David Fiedor ◽  
Renata Pavelková ◽  
Miroslav Trnka
Keyword(s):  
Czech Republic ◽  
Landscape Planning ◽  
Historical Maps ◽  
Water Shortages ◽  
Historical Gis ◽  
The Czech Republic ◽  
Landscape Water ◽  
Agricultural Areas ◽  
Agricultural Regions

Abstract Historical maps are a valuable resource in landscape research. The information gathered from them facilitates the cognisance of landscapes and may assist current landscape planning. This study focuses on the historical occurrence and spatial extent of man-made ponds in the Czech Republic. Based on the 1st Military Survey maps (1764–1783) of the Habsburg Monarchy, we use Historical GIS to identify 7,676 man-made ponds in the historical landscape. Compared to the 2nd Military Survey maps (1836–1852), 56% of these man-made ponds had been drained. Such disappearances mostly affected large ponds in fertile agricultural areas, but also affected small reservoirs in less fertile areas at higher altitudes. As the current maps and spatial datasets (Water reservoirs, Landscape water regime, Farming areas) show, a number of these agricultural regions have been affected by climate changes and face water shortages. The historical map information of former ponds has the potential to contribute to their restoration in areas where water retention in the landscape needs to be increased.

scholarly journals Summarizing the state of the terrestrial biosphere in few dimensions

2019 ◽  
Author(s):  
Guido Kraemer ◽  
Gustau Camps-Valls ◽  
Markus Reichstein ◽  
Miguel D. Mahecha
Keyword(s):  
Data Streams ◽  
Land Surface ◽  
Principal Component ◽  
The State ◽  
Climate Indices ◽  
Surface Dynamics ◽  
Terrestrial Biosphere ◽  
Explanatory Variables ◽  
Arctic Regions ◽  
Agricultural Areas

Abstract. In times of global change, we must closely monitor the state of the planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems – i.e. the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73 % of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions. We assume that this generic approach has great potential for the analysis of land-surface dynamics from observational or model data.

scholarly journals The impact of linear investments on the spatial structure of agricultural regions

2019 ◽  
Vol 86 ◽  
pp. 00011
Author(s):  
Edward Preweda
Keyword(s):  
Spatial Structure ◽  
Rural Areas ◽  
Negative Impact ◽  
Drainage System ◽  
Organic Farms ◽  
The Cost ◽  
Agricultural Areas ◽  
Agricultural Regions ◽  
The Impact ◽  
The Village

Linear investments cause irreversible changes in the existing shape and way of using the land located alongside it. In the case of the construction of the highway, these lands are mostly located in rural areas and prior to construction, they were used mostly in agriculture. Losses resulting from such investments affect the natural environment and landscape. Along the impact zone wind conditions change, also exhaust emissions and noise increase. Investors try to avoid the design of wide protection zones of greenery, due to the cost of buying a larger area of land and they usually use it only when it is necessary. Severe ecological losses result from land degradation, disturbances in the drainage system and changes in water relations. Such investments also have a negative impact on the profitability of agricultural holdings, in particular organic farms. The market value of land adjacent to the motorway is also decreasing. Often, on both sides of the motorway there remain land with a small area, access to the ground is difficult or even impossible. In order to reduce the negative impact of linear investments on the spatial structure of agricultural areas, infrastructure integration is carried out. The implementation of consolidations related to the construction of motorways in Poland is not a common and frequent phenomenon, which lacks concrete plans and schemes of actions. The paper presents the objectives of consolidation in the area of the village of Szczepanow in the Lesser Poland Voivodeship.

scholarly journals Summarizing the state of the terrestrial biosphere in few dimensions

2020 ◽  
Vol 17 (9) ◽  
pp. 2397-2424 ◽  
Author(s):  
Guido Kraemer ◽  
Gustau Camps-Valls ◽  
Markus Reichstein ◽  
Miguel D. Mahecha
Keyword(s):  
Data Streams ◽  
Land Surface ◽  
Principal Component ◽  
The State ◽  
Climate Indices ◽  
Surface Dynamics ◽  
Terrestrial Biosphere ◽  
Explanatory Variables ◽  
Arctic Regions ◽  
Agricultural Areas

Abstract. In times of global change, we must closely monitor the state of the planet in order to understand the full complexity of these changes. In fact, each of the Earth's subsystems – i.e., the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create global indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical state of ecosystems and land–atmosphere fluxes of water, energy, and carbon fluxes. We find that three indicators account for 82 % of the variance of the selected biosphere variables in space and time across the globe. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. The third indicator represents mostly changes in surface albedo. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heat wave (2010). The anomalies also allow us to interpret the impacts of these events. The indicators can also be used to detect and quantify changes in seasonal dynamics. Here we report, for instance, increasing seasonal amplitudes of productivity in agricultural areas and arctic regions. We assume that this generic approach has great potential for the analysis of land surface dynamics from observational or model data.

The Great Migrations and Black Urban Life in the United States, 1914–1970

2018 ◽  
Author(s):  
Tyina Steptoe
Keyword(s):  
United States ◽  
African Americans ◽  
20Th Century ◽  
Black People ◽  
Black Population ◽  
The United States ◽  
Urban Life ◽  
Urban Centers ◽  
Dramatic Shift ◽  
Agricultural Areas

During the 20th century, the black population of the United States transitioned from largely rural to mostly urban. In the early 1900s the majority of African Americans lived in rural, agricultural areas. Depictions of black people in popular culture often focused on pastoral settings, like the cotton fields of the rural South. But a dramatic shift occurred during the Great Migrations (1914–1930 and 1941–1970) when millions of rural black southerners relocated to US cities. Motivated by economic opportunities in urban industrial areas during World Wars I and II, African Americans opted to move to southern cities as well as to urban centers in the Northeast, Midwest, and West Coast. New communities emerged that contained black social and cultural institutions, and musical and literary expressions flourished. Black migrants who left the South exercised voting rights, sending the first black representatives to Congress in the 20th century. Migrants often referred to themselves as “New Negroes,” pointing to their social, political, and cultural achievements, as well as their use of armed self-defense during violent racial confrontations, as evidence of their new stance on race.

Spatial variation of meiofaunal tardigrades in a small tropical estuary (~6°S; Brazil)

2019 ◽  
Vol 70 (8) ◽  
pp. 1094 ◽  
Author(s):  
Sebastião Tilbert ◽  
Francisco J. V. de Castro ◽  
Géssica Tavares ◽  
Miodeli Nogueira Júnior
Keyword(s):  
Redundancy Analysis ◽  
Dry Season ◽  
Spatial Variations ◽  
Total Abundance ◽  
Soft Bottom ◽  
Medium Sand ◽  
Tropical Estuaries ◽  
Explanatory Variables ◽  
Abundant Taxon ◽  
Meiofaunal Abundance

Spatial variations and organism–sediment relationship are paramount subjects of soft-bottom ecology. However, these issues have been unexplored for most minor meiofaunal taxa such as tardigrades. In the present study, we explore this subject on a small tropical (~6°S) estuary. Total meiofaunal abundance ranged from 4 to 1036 individuals per 10cm2, averaging (mean±s.d.) 324.8±245.9 individuals per 10cm2. Nematodes dominated in both seasons, representing &gt;70% of total abundance. Tardigrades were the second-most abundant taxon, representing 15% of the total and up to 71%. Tardigrades were represented by two species, Batillipes dandarae and B. pennaki, the latter dominating in the rainy season, and both with similar abundances in the dry season. Abundance of total meiofauna and both tardigrade species differed significantly (ANOVA; P&lt;0.05) among stations and in the interaction between stations and seasons, but only B. dandarae varied seasonally, with higher values occurring in the dry season. The spatial variations observed were mostly related to differences in the sediment granulometry. Environmental explanatory variables explained 72.6% of the variance of dominant meiofaunal taxa in the Redundancy Analysis. Nematodes and ostracods were mostly associated with fine and very fine sands, both tardigrades with medium sand and oligochaetes with larger size-fractions of the sediment and organic matter. The data gathered here suggest that granulometry was the most important environmental factor in the meiofaunal spatial structure in tropical estuaries and both tardigrade species were closely associated with medium sand.

scholarly journals Buffel grass in Queensland's semi-arid woodlands: response to local and landscape scale variables, and relationship with grass, forb and reptile species

2009 ◽  
Vol 31 (3) ◽  
pp. 293 ◽  
Author(s):  
Teresa J. Eyre ◽  
Jian Wang ◽  
Melanie F. Venz ◽  
Chris Chilcott ◽  
Giselle Whish
Keyword(s):  
Habitat Preferences ◽  
Landscape Scale ◽  
Grazing Pressure ◽  
Critical Threshold ◽  
Hierarchical Partitioning ◽  
Grass Cover ◽  
Explanatory Variables ◽  
Buffel Grass ◽  
Remnant Vegetation ◽  
Reptile Species

Buffel grass [Pennisetum ciliare (L.) Link] has been widely introduced in the Australian rangelands as a consequence of its value for productive grazing, but tends to competitively establish in non-target areas such as remnant vegetation. In this study, we examined the influence landscape-scale and local-scale variables had upon the distribution of buffel grass in remnant poplar box (Eucalyptus populnea F.Muell.) dominant woodland fragments in the Brigalow Bioregion, Queensland. Buffel grass and variables thought to influence its distribution in the region were measured at 60 sites, which were selected based on the amount of native woodland retained in the landscape and patch size. An information-theoretic modelling approach and hierarchical partitioning revealed that the most influential variable was the percent of retained vegetation within a 1-km spatial extent. From this, we identified a critical threshold of ~30% retained vegetation in the landscape, above which the model predicted buffel grass was not likely to occur in a woodland fragment. Other explanatory variables in the model were site based, and included litter cover and long-term rainfall. Given the paucity of information on the effect of buffel grass upon biodiversity values, we undertook exploratory analyses to determine whether buffel grass cover influenced the distribution of grass, forb and reptile species. We detected some trends; hierarchical partitioning revealed that buffel grass cover was the most important explanatory variable describing habitat preferences of four reptile species. However, establishing causal links – particularly between native grass and forb species and buffel grass – was problematic owing to possible confounding with grazing pressure. We conclude with a set of management recommendations aimed at reducing the spread of buffel grass into remnant woodlands.

scholarly journals Determinación de la humedad de suelo mediante regresión lineal múltiple con datos TerraSAR-X

2016 ◽  
pp. 73 ◽  
Author(s):  
G. García ◽  
M. Brogioni ◽  
V. Venturini ◽  
L. Rodriguez ◽  
G. Fontanelli ◽  
...  
Keyword(s):  
Land Surface ◽  
Multiple Linear Regression Model ◽  
Heat Fluxes ◽  
Distributed Data ◽  
Explanatory Variables ◽  
Regression Techniques ◽  
Geophysical Information ◽  
Electromagnetic Models ◽  
Agricultural Regions ◽  
Microwave Sensors

<p>The first five centimeters of soil form an interface where the main heat fluxes exchanges between the land surface and the atmosphere occur. Besides ground measurements, remote sensing has proven to be an excellent tool for the monitoring of spatial and temporal distributed data of the most relevant Earth surface parameters including soil’s parameters. Indeed, active microwave sensors (Synthetic Aperture Radar - SAR) offer the opportunity to monitor soil moisture (HS) at global, regional and local scales by monitoring involved processes. Several inversion algorithms, that derive geophysical information as HS from SAR data, were developed. Many of them use electromagnetic models for simulating the backscattering coefficient and are based on statistical techniques, such as neural networks, inversion methods and regression models. Recent studies have shown that simple multiple regression techniques yield satisfactory results. The involved geophysical variables in these methodologies are descriptive of the soil structure, microwave characteristics and land use. Therefore, in this paper we aim at developing a multiple linear regression model to estimate HS on flat agricultural regions using TerraSAR-X satellite data and data from a ground weather station. The results show that the backscatter, the precipitation and the relative humidity are the explanatory variables of HS. The results obtained presented a RMSE of 5.4 and a R<sup>2</sup>  of about 0.6</p>

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