Large-scale Geographic Pattern of Primate Species Richness in Mainland China at Different Spatial Resolution and Its Relationship to Environmental Factors and Human Activities

2012 ◽  
Vol 18 (6) ◽  
pp. 954 ◽  
Author(s):  
Xiaoqian LIN ◽  
Yong HUANG ◽  
Zhili ZUO ◽  
Yueying CHEN
Author(s):  
Katherine L. Bryant ◽  
Dirk Jan Ardesch ◽  
Lea Roumazeilles ◽  
Lianne H. Scholtens ◽  
Alexandre A. Khrapitchev ◽  
...  

AbstractLarge-scale comparative neuroscience requires data from many species and, ideally, at multiple levels of description. Here, we contribute to this endeavor by presenting diffusion and structural MRI data from eight primate species that have not or rarely been described in the literature. The selected samples from the Primate Brain Bank cover a prosimian, New and Old World monkeys, and a great ape. We present preliminary labelling of the cortical sulci and tractography of the optic radiation, dorsal part of the cingulum bundle, and dorsal parietal–frontal and ventral temporal-frontal longitudinal white matter tracts. Both dorsal and ventral association fiber systems could be observed in all samples, with the dorsal tracts occupying much less relative volume in the prosimian than in other species. We discuss the results in the context of known primate specializations and present hypotheses for further research. All data and results presented here are available online as a resource for the scientific community.


2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Han Gao ◽  
Jinhui Guo ◽  
Peng Guo ◽  
Xiuwan Chen

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).


2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Shuo Xu ◽  
Jie Cheng ◽  
Quan Zhang

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.


1997 ◽  
Vol 100 (1) ◽  
pp. 40 ◽  
Author(s):  
Bruce McCune ◽  
Jonathan P. Dey ◽  
JeriLynn E. Peck ◽  
David Cassell ◽  
Karin Heiman ◽  
...  

2000 ◽  
Vol 51 (2) ◽  
pp. 165 ◽  
Author(s):  
Peter C. Gehrke ◽  
John H. Harris

Riverine fish in New South Wales were studied to examine longitudinal trends in species richness and to identify fish communities on a large spatial scale. Five replicate rivers of four types (montane, slopes, regulated lowland and unregulated lowland) were selected from North Coast, South Coast, Murray and Darling regions. Fishwere sampled during summer and winter in two consecutive years with standardized gear that maximized the range of species caught. The composition of fish communities varied among regions and river types, with little temporal variation. Distinct regional communities converged in montane reaches and diverged downstream. The fish fauna can be classified into North Coast, South Coast, Murray and Darling communities, with a distinct montane community at high elevations irrespective of the drainage division. Species richness increased downstream in both North Coast and South Coast regions by both replacement and the addition of new species. In contrast, species richness in the Darling and Murray regions reached a maximum in the slopes reaches and then declined, reflecting a loss of species in lowland reaches. The small number of species is typical of the freshwater fish faunas of similar climatic regions world-wide. Fish communities identified in this study form logical entities for fisheries management consistent with the ecosystem-focused, catchment-based approach to river management and water reform being adopted in Australia.


2017 ◽  
Vol 21 (1) ◽  
pp. 117-132 ◽  
Author(s):  
Jannis M. Hoch ◽  
Arjen V. Haag ◽  
Arthur van Dam ◽  
Hessel C. Winsemius ◽  
Ludovicus P. H. van Beek ◽  
...  

Abstract. Large-scale flood events often show spatial correlation in neighbouring basins, and thus can affect adjacent basins simultaneously, as well as result in superposition of different flood peaks. Such flood events therefore need to be addressed with large-scale modelling approaches to capture these processes. Many approaches currently in place are based on either a hydrologic or a hydrodynamic model. However, the resulting lack of interaction between hydrology and hydrodynamics, for instance, by implementing groundwater infiltration on inundated floodplains, can hamper modelled inundation and discharge results where such interactions are important. In this study, the global hydrologic model PCR-GLOBWB at 30 arcmin spatial resolution was one-directionally and spatially coupled with the hydrodynamic model Delft 3D Flexible Mesh (FM) for the Amazon River basin at a grid-by-grid basis and at a daily time step. The use of a flexible unstructured mesh allows for fine-scale representation of channels and floodplains, while preserving a coarser spatial resolution for less flood-prone areas, thus not unnecessarily increasing computational costs. In addition, we assessed the difference between a 1-D channel/2-D floodplain and a 2-D schematization in Delft 3D FM. Validating modelled discharge results shows that coupling PCR-GLOBWB to a hydrodynamic routing scheme generally increases model performance compared to using a hydrodynamic or hydrologic model only for all validation parameters applied. Closer examination shows that the 1-D/2-D schematization outperforms 2-D for r2 and root mean square error (RMSE) whilst having a lower Kling–Gupta efficiency (KGE). We also found that spatial coupling has the significant advantage of a better representation of inundation at smaller streams throughout the model domain. A validation of simulated inundation extent revealed that only those set-ups incorporating 1-D channels are capable of representing inundations for reaches below the spatial resolution of the 2-D mesh. Implementing 1-D channels is therefore particularly of advantage for large-scale inundation models, as they are often built upon remotely sensed surface elevation data which often enclose a strong vertical bias, hampering downstream connectivity. Since only a one-directional coupling approach was tested, and therefore important feedback processes are not incorporated, simulated discharge and inundation extent for both coupled set-ups is generally overpredicted. Hence, it will be the subsequent step to extend it to a two-directional coupling scheme to obtain a closed feedback loop between hydrologic and hydrodynamic processes. The current findings demonstrating the potential of one-directionally and spatially coupled models to obtain improved discharge estimates form an important step towards a large-scale inundation model with a full dynamic coupling between hydrology and hydrodynamics.


1987 ◽  
Vol 35 (2) ◽  
pp. 135 ◽  
Author(s):  
RB Hacker

Species responses to grazing and environmental factors were studied in an arid halophytic shrubland community in Western Australia. The grazing responses of major shrub species were defined by using reciprocal averaging ordination of botanical data, interpreted in conjunction with a similar ordination of soil chemical properties and measures of soil erosion derived from large-scale aerial photographs. An apparent small-scale interaction between grazing and soil salinity was also defined. Long-term grazing pressure is apparently reduced on localised areas of high salinity. Environmental factors affecting species distribution are complex and appear to include soil salinity, soil cationic balance, geomorphological variation and the influence of cryptogamic crusts on seedling establishment.


2018 ◽  
Vol 24 (10) ◽  
pp. 1464-1477 ◽  
Author(s):  
Alexa J. McKerrow ◽  
Nathan M. Tarr ◽  
Matthew J. Rubino ◽  
Steven G. Williams

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