Effects of adaptive multiple paddock and continuous grazing on fine-scale spatial patterns of vegetation species and biomass in commercial ranches

2021 ◽  
Author(s):  
Fugui Wang ◽  
Steven I. Apfelbaum ◽  
Ry L. Thompson ◽  
Richard Teague ◽  
Peter Byck
2001 ◽  
Vol 268 (1468) ◽  
pp. 711-717 ◽  
Author(s):  
P. P. Pomeroy ◽  
J. Worthington Wilmer ◽  
W. Amos ◽  
S. D. Twiss

2012 ◽  
Vol 13 (1) ◽  
pp. 28 ◽  
Author(s):  
S. E. Everhart ◽  
A. Askew ◽  
L. Seymour ◽  
T. C. Glenn ◽  
H. Scherm

To better understand the fine-scale spatial dynamics of brown rot disease and corresponding fungal genotypes, we analyzed three-dimensional spatial patterns of pre-harvest fruit rot caused by Monilinia fructicola in individual peach tree canopies and developed microsatellite markers for canopy-level population genetics analyses. Using a magnetic digitizer, high-resolution maps of fruit rot development in five representative trees were generated, and M. fructicola was isolated from each affected fruit. To characterize disease aggregation, nearestneighbor distances among symptomatic fruit were calculated and compared with appropriate random simulations. Within-canopy disease aggregation correlated negatively with the number of diseased fruit per tree (r = −0.827, P = 0.0009), i.e., aggregation was greatest when the number of diseased fruit was lowest. Sixteen microsatellite primers consistently amplified polymorphic regions in a geographically diverse test population of 47 M. fructicola isolates. None of the test isolates produced identical multilocus genotypes, and the number of alleles per locus ranged from 2 to 16. We are applying these markers to determine fine-scale population structure of the pathogen within and among canopies. Accepted for publication 23 May 2012. Published 23 July 2012.


2015 ◽  
Vol 96 (6) ◽  
pp. 1194-1202 ◽  
Author(s):  
Brian Keane ◽  
Shavonne Ross ◽  
Thomas O. Crist ◽  
Nancy G. Solomon

2017 ◽  
Vol 93 (10) ◽  
Author(s):  
Jie-Liang Liang ◽  
Xiao-Jing Li ◽  
Hao-Yue Shu ◽  
Pandeng Wang ◽  
Jia-Liang Kuang ◽  
...  

2016 ◽  
Vol 74 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Maria Mateo ◽  
Lionel Pawlowski ◽  
Marianne Robert

Efficiency of mixed-fisheries management and operational implementation of the ecosystem approach to fisheries management rely on the ability to understand and describe the technical and biological interactions between fleets, gears and species. The present study aims to describe fine-scale spatial patterns of the French demersal mixed fisheries in the Celtic Sea and discusses their implications in terms of management. Analysis was made by integrating vessel monitoring systems and logbook data collected between 2010 and 2012 at a 3′*3′ spatial scale through the use of principal component analysis followed by hierarchical clustering. It revealed spatial regions defined by a distinct homogeneous composition of retained catches. Each cluster was also described in terms of the fishing activity: vessel length, effort, power and gear used. The analysis revealed a complex spatial structure in the species assemblage caught and suggests that a single situation cannot describe the mixed fisheries of the Celtic Sea, but rather that there are several distinct cases of mixed fisheries. Our results also highlight the limitations of using the current level of data aggregation commonly requested in international data calls to model these fisheries and suggest that improvements should be made to ensure efficient evaluation of management options. Analyses of spatially resolved fisheries data such as the one presented here open a range of potential applications. In the context of the Common Fisheries Policy reform and the landing obligation, comparison of our results with applications of the same methodology to a subset of vulnerable species or to catches of fish below the minimum conservation reference size would help to identify the geographical areas to avoid and assess potential effort reallocation strategies based on groups of target species.


2020 ◽  
Author(s):  
Gregory F Albery ◽  
Amy R Sweeny ◽  
Daniel J Becker ◽  
Shweta Bansal

AbstractAll pathogens are heterogeneous in space, yet little is known about the prevalence and scale of this spatial variation, particularly in wild animal systems. To address this question, we conducted a broad literature search to identify datasets involving diseases of wild mammals in spatially distributed contexts. Across 31 such final datasets featuring 89 replicates and 71 host-parasite combinations, only 51% had previously been used to test spatial hypotheses. We analysed these datasets for spatial dependence within a standardised modelling framework using Bayesian linear models. We detected spatial autocorrelation in 44/89 model replicates (54%) across 21/31 datasets (68%), spread across parasites of all groups and transmission modes. Surprisingly, although larger sampling areas more easily detected spatial patterns, even some very small study areas (under 0.01km2) exhibited substantial spatial heterogeneity. Parasites of all transmission modes had easily detectable spatial patterns, implying that structured contact networks and susceptibility effects are likely as important in spatially structuring disease as are environmental drivers of transmission efficiency. Our findings imply that fine-scale spatial patterns of infection often manifest in wild animal systems, whether or not the aim of the study is to examine environmentally varying processes. Given the widespread nature of these findings, studies should more frequently record and analyse spatial data, facilitating development and testing of spatial hypotheses in disease ecology.


2012 ◽  
Vol 9 (1) ◽  
pp. 457-475 ◽  
Author(s):  
S. M. Gourdji ◽  
K. L. Mueller ◽  
V. Yadav ◽  
D. N. Huntzinger ◽  
A. E. Andrews ◽  
...  

Abstract. Atmospheric inversion models have the potential to quantify CO2 fluxes at regional, sub-continental scales by taking advantage of near-surface CO2 mixing ratio observations collected in areas with high flux variability. This study presents results from a series of regional geostatistical inverse models (GIM) over North America for 2004, and uses them as the basis for an inter-comparison to other inversion studies and estimates from biospheric models collected through the North American Carbon Program Regional and Continental Interim Synthesis. Because the GIM approach does not require explicit prior flux estimates and resolves fluxes at fine spatiotemporal scales (i.e. 1° × 1°, 3-hourly in this study), it avoids temporal and spatial aggregation errors and allows for the recovery of realistic spatial patterns from the atmospheric data relative to previous inversion studies. Results from a GIM inversion using only available atmospheric observations and a fine-scale fossil fuel inventory were used to confirm the quality of the inventory and inversion setup. An inversion additionally including auxiliary variables from the North American Regional Reanalysis found inferred relationships with flux consistent with physiological understanding of the biospheric carbon cycle. Comparison of GIM results with bottom-up biospheric models showed stronger agreement during the growing relative to the dormant season, in part because most of the biospheric models do not fully represent agricultural land-management practices and the fate of both residual biomass and harvested products. Comparison to earlier inversion studies pointed to aggregation errors as a likely source of bias in previous sub-continental scale flux estimates, particularly for inversions that adjust fluxes at the coarsest scales and use atmospheric observations averaged over long periods. Finally, whereas the continental CO2 boundary conditions used in the GIM inversions have a minor impact on spatial patterns, they have a substantial impact on the continental carbon budget, with a difference of 0.8 PgC yr−1 in the total continental flux resulting from the use of two plausible sets of boundary CO2 mixing ratios. Overall, this inter-comparison study helps to assess the state of the science in estimating regional-scale CO2 fluxes, while pointing towards the path forward for improvements in future top-down and bottom-up modeling efforts.


2014 ◽  
Vol 8 (8) ◽  
pp. 1715-1726 ◽  
Author(s):  
Gavin Lear ◽  
Julia Bellamy ◽  
Bradley S Case ◽  
Jack E Lee ◽  
Hannah L Buckley

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