scholarly journals Large‐scale spatial variation in feather corticosterone in invasive house sparrows ( P asser domesticus ) in M exico is related to climate

2015 ◽  
Vol 5 (17) ◽  
pp. 3808-3817 ◽  
Author(s):  
Gillian D. Treen ◽  
Keith A. Hobson ◽  
Tracy A. Marchant ◽  
Gary R. Bortolotti
Keyword(s):  
Spatial Variation ◽  
Large Scale ◽  
House Sparrows ◽  
Feather Corticosterone

scholarly journals Spatial variation and mechanisms of leaf water content in grassland plants at the biome scale: evidence from three comparative transects

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruomeng Wang ◽  
Nianpeng He ◽  
Shenggong Li ◽  
Li Xu ◽  
Mingxu Li
Keyword(s):  
Spatial Variation ◽  
Water Content ◽  
Large Scale ◽  
Life Forms ◽  
Important Variable ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Desert Grassland ◽  
Large Spatial Scale ◽  
Grassland Plants

AbstractLeaf water content (LWC) has important physiological and ecological significance for plant growth. However, it is still unclear how LWC varies over large spatial scale and with plant adaptation strategies. Here, we measured the LWC of 1365 grassland plants, along three comparative precipitation transects from meadow to desert on the Mongolia Plateau (MP), Loess Plateau, and Tibetan Plateau, respectively, to explore its spatial variation and the underlying mechanisms that determine this variation. The LWC data were normally distributed with an average value of 0.66 g g−1. LWC was not significantly different among the three plateaus, but it differed significantly among different plant life forms. Spatially, LWC in the three plateaus all decreased and then increased from meadow to desert grassland along a precipitation gradient. Unexpectedly, climate and genetic evolution only explained a small proportion of the spatial variation of LWC in all plateaus, and LWC was only weakly correlated with precipitation in the water-limited MP. Overall, the lasso variation in LWC with precipitation in all plateaus represented an underlying trade-off between structural investment and water income in plants, for better survival in various environments. In brief, plants should invest less to thrive in a humid environment (meadow), increase more investment to keep a relatively stable LWC in a drying environment, and have high investment to hold higher LWC in a dry environment (desert). Combined, these results indicate that LWC should be an important variable in future studies of large-scale trait variations.

scholarly journals Mapping the spatial variation of soil moisture at the large scale using GPR for pavement applications

2014 ◽  
Vol 13 (3) ◽  
pp. 269-278 ◽  
Author(s):  
Andrea Benedetto ◽  
Fabio Tosti ◽  
Bianca Ortuani ◽  
Mauro Giudici ◽  
Mauro Mele
Keyword(s):  
Soil Moisture ◽  
Spatial Variation ◽  
Large Scale

scholarly journals Herbivory drives large‐scale spatial variation in reef fish trophic interactions

2014 ◽  
Vol 4 (23) ◽  
pp. 4553-4566 ◽  
Author(s):  
Guilherme O. Longo ◽  
Carlos Eduardo L. Ferreira ◽  
Sergio R. Floeter
Keyword(s):  
Spatial Variation ◽  
Reef Fish ◽  
Trophic Interactions ◽  
Large Scale

scholarly journals Large-scale spatial variation of chronic stress signals in moose

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0225990
Author(s):  
Göran Spong ◽  
Nicholas P. Gould ◽  
Ellinor Sahlén ◽  
Joris P. G. M. Cromsigt ◽  
Jonas Kindberg ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Chronic Stress ◽  
Large Scale ◽  
Stress Signals

Bioturbation, microbial activity and sediment properties in an estuarine ecosystem

1986 ◽  
Vol 90 ◽  
pp. 129-142 ◽  
Author(s):  
P. S. Meadows ◽  
A. Tufail
Keyword(s):  
Spatial Variation ◽  
Microbial Activity ◽  
Large Scale ◽  
Chemical Properties ◽  
Estuarine Sediments ◽  
Sediment Stability ◽  
Estuarine Ecosystem ◽  
Particle Sedimentation ◽  
Geochemical Properties ◽  
Sea Bed

SynopsisMacrobenthic bioturbation and microbial activity can have major ecological effects on present day marine sediments some of which are also seen in the fossil record. The coastal and estuarine sediments in the Clyde Estuary area are described. They contain very high densities of benthic macroorganisms and microorganisms. These organisms have dramatic effects on the physical and chemical properties of sediments, and field and laboratory studies have shown that many geotechnical and geochemical properties of sediments are dictated by them. The effects can be on a microscale of a few millimetres horizontally or vertically (micro-spatial variation), or can have large scale consequences over hundreds of metres (macro-spatial variation).Sediment stability (shear strength and critical erosion velocity), permeability and particle sedimentation are all altered by macro-invertebrate bioturbation or microbial activity, and many chemical properties such as Eh and pH may be influenced in a similar manner.Biological activity may therefore play a major role in modifying sediment geotechnics and early sediment diagenesis. It also has direct relevance to the siting of man-made structures on the sea-bed and to sediment stability and transport in estuaries.In situseeding experiments may lead to biological control and manipulation of these most important field phenomena.

A large-scale survey of house sparrows feathers reveals ubiquitous presence of neonicotinoids in farmlands

2019 ◽  
Vol 660 ◽  
pp. 1091-1097 ◽  
Author(s):  
Ségolène Humann-Guilleminot ◽  
Sarah Clément ◽  
Julia Desprat ◽  
Łukasz J. Binkowski ◽  
Gaétan Glauser ◽  
...  
Keyword(s):  
Large Scale ◽  
House Sparrows ◽  
Large Scale Survey ◽  
Ubiquitous Presence

scholarly journals Assessment of the Use of Geographically Weighted Regression for Analysis of Large On-Farm Experiments and Implications for Practical Application

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1720
Author(s):  
Fiona H. Evans ◽  
Angela Recalde Salas ◽  
Suman Rakshit ◽  
Craig A. Scanlan ◽  
Simon E. Cook
Keyword(s):  
Spatial Variation ◽  
Geographically Weighted Regression ◽  
Large Scale ◽  
Treatment Effects ◽  
Management Practices ◽  
Response To Treatment ◽  
Weighted Regression ◽  
Yield Response ◽  
Practical Application ◽  
On Farm

On-farm experimentation (OFE) is a farmer-centric process that can enhance the adoption of digital agriculture technologies and improve farm profitability and sustainability. Farmers work with consultants or researchers to design and implement experiments using their own machinery to test management practices at the field or farm scale. Analysis of data from OFE is challenging because of the large spatial variation influenced by spatial autocorrelation that is not due to the treatment being tested and is often much larger than treatment effects. In addition, the relationship between treatment and yield response may also vary spatially. We investigate the use of geographically weighted regression (GWR) for analysis of data from large on-farm experiments. GWR estimates local regressions, where data are weighted by distance from the site using a distance-decay kernel. It is a simple approach that can be easily explained to farmers and their agronomic advisors. We use simulated data to test the ability of GWR to separate yield variation due to treatment from any underlying spatial variation in yield that is not due to treatment; show that GWR kernel bandwidth can be based on experimental design to accurately separate the underlying spatial variability from treatment effects; and demonstrate a step-wise model selection approach to determine when the response to treatment is global across the experiment or locally varying. We demonstrate our recommended approach on two large-scale experiments conducted on farms in Western Australia to investigate grain yield response to potassium fertiliser. We discuss the implications of our results for routine practical application to OFE and conclude that GWR has potential for wide application in a semi-automated manner to analyse OFE data, improve farm decision-making, and enhance the adoption of digital technologies.

scholarly journals Vegetation classification enables inferring mesoscale spatial variation in plant invasibility

2019 ◽  
Vol 12 (03) ◽  
pp. 161-168
Author(s):  
Yue M. Li ◽  
Brett Stauffer ◽  
Jim Malusa
Keyword(s):  
Spatial Variation ◽  
Invasive Plants ◽  
Large Scale ◽  
Vegetation Classification ◽  
Invasion Risk ◽  
Logistic Regression Models ◽  
Reliable Assessment ◽  
Brassica Tournefortii ◽  
Classification Project ◽  
Source Populations

AbstractLarge-scale control of invasive plants can benefit strongly from reliable assessment of spatial variation in plant invasibility. With this knowledge, limited management resources can be concentrated in areas of high invasion risk. We assessed the influence of spatial environments and proximity to roads on the invasibility of African mustard (Brassica tournefortii Gouan) over the 280,000-ha Barry M. Goldwater Range West in southwestern Arizona, USA. We used presence/absence data of B. tournefortii acquired from a vegetation classification project, in which lands were mapped to the level of vegetation subassociations. Logistic regression models suggested that spatial environments represented by the subassociations, not proximity to roads, represented the only factor significantly explaining B. tournefortii presence. We then used the best model to predict B. tournefortii invasibility in each subassociation. This prediction indicates management strategy should differ between the western part and the central to eastern part of the range. The western range is a large spatial continuum with intermediate to high invasion risk, vulnerable to an untethered spread of B. tournefortii. Controlling efforts should focus on preventing existing local populations from further expansion. The central and eastern ranges are a mosaic varying strongly in invasion risk. Control efforts can take advantage of natural invasion barriers and further reduce connectivity through removal of source populations connected with other high-risk locations via roads and other dispersal corridors. We suggest our approach as one effective way to combine vegetation classification and plant invasion assessment to manage complex landscapes over large ranges, especially when this approach is used through an iterative prediction–validation process to achieve adaptive management of invasive plants.

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