scholarly journals Conservation guidelines for the endangered Brown-eared pheasant based on the geographic information system and the MaxEnt model

2016 ◽  
Author(s):  
Yilin Li ◽  
Xinhai Li ◽  
Zitan Song ◽  
Changqing Ding
Keyword(s):  
Species Distribution ◽  
Forest Cover ◽  
Distribution Range ◽  
Shanxi Province ◽  
Distribution Area ◽  
Beijing City ◽  
Minimum Convex Polygon ◽  
Maxent Model ◽  
Historical Distribution ◽  
Forest Coverage

We analyzed the synchronous relationship between forest cover and species distribution to explain the contraction in the distribution range of the brown-eared pheasant (Crossoptilon mantchuricum) in China. We consulted ancient texts to determine this pheasant’s historical distribution from 25 to 1947 CE. Based on this species’ habitat selection criteria, the history of the forests, ancient climate change records, and fossil data, we determined that the brown-eared pheasant’s historical distribution included the three provinces of Shaanxi, Shanxi, and Hebei. It once inhabited an area of about 320,000 km2, as calculated by the minimum convex polygon method (MCP) in ArcGIS 10.0. The current species distribution covers 46,800 km2 of the Shaanxi, Shanxi, and Hebei provinces, as well as Beijing city, while Shanxi remains the center of the distribution area. This pheasant’s distribution range has decreased by 85% over the past 2,000 years. We used the Mean Decrease Accuracy (MDA) index to assess the importance of the evaluation of 13 environmental factors using the Random Forests (RF) measure from the R 3.0.2 software platform. The results showed that vegetation is the most important determinant influencing distribution. We built a corresponding correlative relationship between the presence/absence of brown-eared pheasant and forest coverage and found that forest coverage in the north, northeast, central, and southeast areas of the Shanxi province were all less than 10% at the end of the Qing Dynasty (1911 CE). Our MaxEnt model indicated that the brown-eared pheasant had retreated to the western regions of Shanxi (AUC = 0.753) and that the historical distribution area had reduced synchronously with the disappearance of local forest cover in Shanxi. Wild brown-eared pheasant populations are stable in the Luliang Mountains, where forest coverage reached 13.2% in 2000. Consequently, we concluded that the distribution of this species is primarily determined by vegetation conditions and that forest cover was the most significant determining factor. To guarantee stable growth in the population and consistent distribution of the brown-eared pheasant, we suggest that forest coverage should be at least 48% in the natural reserves where the brown-eared pheasant is currently distributed.

scholarly journals Conservation guidelines for the endangered Brown-eared pheasant based on the geographic information system and the MaxEnt model

2016 ◽  
Author(s):  
Yilin Li ◽  
Xinhai Li ◽  
Zitan Song ◽  
Changqing Ding
Keyword(s):  
Species Distribution ◽  
Forest Cover ◽  
Distribution Range ◽  
Shanxi Province ◽  
Distribution Area ◽  
Beijing City ◽  
Minimum Convex Polygon ◽  
Maxent Model ◽  
Historical Distribution ◽  
Forest Coverage

We analyzed the synchronous relationship between forest cover and species distribution to explain the contraction in the distribution range of the brown-eared pheasant (Crossoptilon mantchuricum) in China. We consulted ancient texts to determine this pheasant’s historical distribution from 25 to 1947 CE. Based on this species’ habitat selection criteria, the history of the forests, ancient climate change records, and fossil data, we determined that the brown-eared pheasant’s historical distribution included the three provinces of Shaanxi, Shanxi, and Hebei. It once inhabited an area of about 320,000 km2, as calculated by the minimum convex polygon method (MCP) in ArcGIS 10.0. The current species distribution covers 46,800 km2 of the Shaanxi, Shanxi, and Hebei provinces, as well as Beijing city, while Shanxi remains the center of the distribution area. This pheasant’s distribution range has decreased by 85% over the past 2,000 years. We used the Mean Decrease Accuracy (MDA) index to assess the importance of the evaluation of 13 environmental factors using the Random Forests (RF) measure from the R 3.0.2 software platform. The results showed that vegetation is the most important determinant influencing distribution. We built a corresponding correlative relationship between the presence/absence of brown-eared pheasant and forest coverage and found that forest coverage in the north, northeast, central, and southeast areas of the Shanxi province were all less than 10% at the end of the Qing Dynasty (1911 CE). Our MaxEnt model indicated that the brown-eared pheasant had retreated to the western regions of Shanxi (AUC = 0.753) and that the historical distribution area had reduced synchronously with the disappearance of local forest cover in Shanxi. Wild brown-eared pheasant populations are stable in the Luliang Mountains, where forest coverage reached 13.2% in 2000. Consequently, we concluded that the distribution of this species is primarily determined by vegetation conditions and that forest cover was the most significant determining factor. To guarantee stable growth in the population and consistent distribution of the brown-eared pheasant, we suggest that forest coverage should be at least 48% in the natural reserves where the brown-eared pheasant is currently distributed.

scholarly journals Determining the distribution loss of brown eared-pheasant (Crossoptilon mantchuricum) using historical data and potential distribution estimates

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2556
Author(s):  
Yilin Li ◽  
Xinhai Li ◽  
Zitan Song ◽  
Changqing Ding
Keyword(s):  
Species Distribution ◽  
Forest Cover ◽  
Shanxi Province ◽  
Distribution Area ◽  
Beijing City ◽  
Maxent Model ◽  
The North ◽  
Historical Distribution ◽  
Forest Coverage ◽  
Historical Resources

We analyzed the synchronous relationship between forest cover and species distribution to explain the contraction in the distribution range of the brown eared-pheasant (Crossoptilon mantchuricum) in China. Historical resources can provide effective records for reconstructing long-term distribution dynamics. The brown eared-pheasant’s historical distribution from 25 to 1947 CE, which included the three provinces of Shaanxi, Shanxi, and Hebei based on this species’ habitat selection criteria, the history of the forests, ancient climate change records, and fossil data. The current species distribution covers Shaanxi, Shanxi, and Hebei provinces, as well as Beijing city, while Shanxi remains the center of the distribution area. MaxEnt model indicated that the suitable conditions of the brown eared-pheasant had retreated to the western regions of Shanxi and that the historical distribution area had reduced synchronously with the disappearance of local forest cover in Shanxi. We built a correlative relationship between the presence/absence of brown eared-pheasants and forest coverage and found that forest coverage in the north, northeast, central, and southeast areas of the Shanxi province were all less than 10% in 1911. Wild brown eared-pheasants are stable in the Luliang Mountains, where forest coverage reached 13.2% in 2000. Consequently, we concluded that the distribution of this species is primarily determined by vegetation conditions and that forest cover was the most significant determining factor.

scholarly journals Predicting the Distribution of the Invasive Species Leptocybe invasa: Combining MaxEnt and Geodetector Models

Insects ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 92
Author(s):  
Hua Zhang ◽  
Jinyue Song ◽  
Haoxiang Zhao ◽  
Ming Li ◽  
Wuhong Han
Keyword(s):  
Roc Curve ◽  
Potential Distribution ◽  
Distribution Range ◽  
Invasive Pest ◽  
Distribution Area ◽  
Total Contribution ◽  
Actual Distribution ◽  
Maxent Model ◽  
Average Temperature ◽  
Leptocybe Invasa

Leptocybe invasa is a globally invasive pest of eucalyptus plantations, and is steadily spread throughout China. Predicting the growth area of L. invasa in China is beneficial to the establishment of early monitoring, forecasting, and prevention of this pest. Based on 194 valid data points and 21 environmental factors of L. invasa in China, this study simulated the potential distribution area of L. invasa in China under three current and future climate scenarios (SSPs1–2.5, SSPs2–3.5, and SSPs5–8.5) via the MaxEnt model. The study used the species distribution model (SDM) toolbox in ArcGIS software to analyze the potential distribution range and change of L. invasa. The importance of crucial climate factors was evaluated by total contribution rate, knife-cut method, and environmental variable response curve, and the area under the receiver operating characteristic (ROC) curve was used to test and evaluate the accuracy of the model. The results showed that the simulation effect of the MaxEnt model is excellent (area under the ROC curve (AUC) = 0.982). The prediction showed that L. invasa is mainly distributed in Guangxi, Guangdong, Hainan, and surrounding provinces, which is consistent with the current actual distribution range. The distribution area of the potential high fitness zone of L. invasa in the next three scenarios increases by between 37.37% and 95.20% compared with the current distribution. Climate change affects the distribution of L. invasa, with the annual average temperature, the lowest temperature of the coldest month, the average temperature of the driest season, the average temperature of the coldest month, and the precipitation in the wettest season the most important. In the future, the core areas of the potential distribution of L. invasa in China will be located in Yunnan, Guangxi, Guangdong, and Hainan. They tend to spread to high latitudes (Hubei, Anhui, Zhejiang, Jiangsu, and other regions).

STUDY ON THE DISTRIBUTION AND HABITAT CHARACTERISTICS OF BRANDT’S VOLE

2015 ◽  
Vol 13 (2) ◽  
pp. 88-95 ◽  
Author(s):  
N Enkhbold ◽  
Shi Dazhao ◽  
N Batsaikhan ◽  
Deng Wang ◽  
D Tseveendorj ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Habitat Characteristics ◽  
Shanxi Province ◽  
Distribution Map ◽  
Grassland Ecosystems ◽  
Historical Distribution ◽  
Recent Climate ◽  
Xinjiang Uyghur Autonomous Region ◽  
Brandt’S Vole

Many species have suffered changed habitable area due to recent climate change or/and human activities. Brandt’s vole (Lasiopodomys brandtii) is same as it.The species is undergoing a continuous habitat change as a consequence of the human’s production, climate change and retrogressive succession of grassland ecosystems. The distribution of the Brandt’s vole was update in China and Mongolia. Based on the historical distribution map, which was made by overlaying the historical data with vegetation type’s map of the grassland resources in China and Mongolia. And the distribution map was modified according to the on-site investigate data in recent years. The current species’ distribution was range Hulunbei’er, northern Xilinguole, which comprise a landscape through the east of Dornod Aimag, Mongolia, and southern Hunshandake Sandy Land in China which just includes the areas around the Zhengxiangbai Qi. There were no the species in the east of Daxinganling Mountains, Liaoning, Shanxi province and Xinjiang Uyghur Autonomous Region of China. The species’ distribution is discontinuous in Mongolia, including western, middle and eastern Mongolia. The western distribution along Hangay Mountains and extends for the west, the northern boundary extended to between Ider river and Chulouk river. Southward reaches of the Malhin along the plain between Hangay Mountain and Altay Mountain. The middle and east of the species’ range lie in the part areas of Tov, Hentiy, Dundgovi, Dornogovi. extends to Hulunbei’er and northern Xilinguole eastwardly and southwardly respectively, northward reaches of the Trans-Baikalia, Russia.Mongolian Journal of Agricultural Sciences Vol.13(2) 2014: 88-95

scholarly journals MaxEnt Modeling Based on CMIP6 Models to Project Potential Suitable Zones for Cunninghamia lanceolata in China

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 752
Author(s):  
Yichen Zhou ◽  
Zengxin Zhang ◽  
Bin Zhu ◽  
Xuefei Cheng ◽  
Liu Yang ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Water Conservation ◽  
Critical Role ◽  
Cunninghamia Lanceolata ◽  
Distribution Area ◽  
Timber Species ◽  
Diurnal Range ◽  
The Future ◽  
Suitable Area

Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% of the overall afforested area. Moreover, it plays a critical role in soil and water conservation; however, its suitability is subject to climate change. For this study, the appropriate distribution area of C. lanceolata was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed that (1) the minimum temperature of the coldest month (bio6), and the mean diurnal range (bio2) were the most important environmental variables that affected the distribution of C. lanceolata; (2) the currently suitable areas of C. lanceolata were primarily distributed along the southern coastal areas of China, of which 55% were moderately so, while only 18% were highly suitable; (3) the projected suitable area of C. lanceolata would likely expand based on the BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under different SSPs spanning 2041–2060. The increased area estimated for the future ranged from 0.18 to 0.29 million km2, where the total suitable area of C. lanceolata attained a maximum value of 2.50 million km2 under the SSP3-7.0 scenario, with a lowest value of 2.39 million km2 under the SSP5-8.5 scenario; (4) in combination with land use and farmland protection policies of China, it is estimated that more than 60% of suitable land area could be utilized for C. lanceolata planting from 2041–2060 under different SSP scenarios. Although climate change is having an increasing influence on species distribution, the deleterious impacts of anthropogenic activities cannot be ignored. In the future, further attention should be paid to the investigation of species distribution under the combined impacts of climate change and human activities.

scholarly journals When Density Matters: The Spatial Balance between Early and Latewood

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 818
Author(s):  
Maria Royo-Navascues ◽  
Edurne Martinez del Castillo ◽  
Roberto Serrano-Notivoli ◽  
Ernesto Tejedor ◽  
Klemen Novak ◽  
...  
Keyword(s):  
Species Distribution ◽  
Pinus Halepensis ◽  
Environmental Gradients ◽  
Temporal Variations ◽  
Western Mediterranean ◽  
Climatic Conditions ◽  
Growth Patterns ◽  
Distribution Area ◽  
Mediterranean Forests ◽  
Climatic Influence

Understanding the influence of the current climate on the distribution, composition, and carbon storage capacity of Mediterranean tree species is key to determining future pathways under a warmer and drier climate scenario. Here, we evaluated the influence of biotic and environmental factors on earlywood (EW) and latewood (LW) growth in Aleppo pine (Pinus halepensis Mill.). Our investigation was based on a dense dendrochronological network (71 sites), which covered the entire distribution area of the species in the Iberian Peninsula (around 119.652 km2), and a high-resolution climate dataset of the Western Mediterranean area. We used generalized linear-mixed models to determine the spatial and temporal variations of EW and LW across the species distribution. Our results showed an intense but differentiated climatic influence on both EW and LW growth components. The climatic influence explained significant variations across the environmental gradients in the study area, which suggested an important adaptation through phenotypic plasticity and local adaptation to varying climatic conditions. In addition, we detected a clear spatial trade-off between efficiency and safety strategy in the growth patterns across the species distribution. Additionally, in more productive areas, the trees presented a higher proportion of EW (more efficient to water transport), while, in more xeric conditions, the LW proportion increased (more safety to avoid embolisms), implying an adaptation to more frequent drought episodes and a higher capacity of carbon depletion. We therefore concluded that Mediterranean forests adapted to dryer conditions might be more efficient as carbon reservoirs than forests growing in wetter areas. Finally, we advocated for the need to consider wood density (EW/LW proportion) when modeling current and future forest carbon sequestrations.

scholarly journals Analysis on the Riding Characteristics of Mobike Sharing Bicycle in Beijing City, China

2019 ◽  
Vol 1 ◽  
pp. 1-2 ◽  
Author(s):  
Min Cao ◽  
Mengxue Huang
Keyword(s):  
Kernel Density ◽  
Spatial Behavior ◽  
Distribution Area ◽  
Beijing City ◽  
Spatial Characteristics ◽  
Morning Peak ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics ◽  
Shared Bicycle ◽  
Evening Peak

<p><strong>Abstract.</strong> The development of the sharing economy has provided an important realization path for urban’s green and healthy development, and has also accelerated the speed of urban development. With the constant capital pouring into the public transport field, dock-less shared bicycle is a relatively new form of transport in urban areas, and it provides a bikesharing service to fulfil urban short trips. Dock-less shared bicycle, with a characteristic of riding and stopping anywhere, has successfully solved the last mile travel problem. Recently, studies focus on the on the temporal spatial characteristics of public bicycle based on public bicycle operation data. However, there are few studies on the identification of riding patterns based on the characteristics of temporal and spatial behavior of residents. In addition, researches have been conducted on public bicycles administered by the government, and the dock-less shared bicycle have different characteristics from public bicycles in terms of scale of use and mode of use. This paper aims to analyze the temporal and spatial characteristics of residents using shared bicycles, and attempts to explore the characteristics of the riding modes of the dock-less shared bicycles.</p><p>Mobike sharing bicycle dataset of Beijing city were obtained for the research and this dataset contains a wealth of attributes with cover of 396600 shared bicycle users and 485500 riding records from May 10 to May 25 in 2017. Additionally, 19 types of POI (Point of Interest) data were also obtained through the API of Baidu Maps. To examine the patterns of shared bicycle trips, these POI data are categorized into five types including residential, commercial, institution, recreation and transport. Spatiotemporal analysis method, correlation analysis methods and kernel density methods were used to analyse the temporal and spatial characteristics of shared bicycle trips, revealing the time curve and spatial hotspot distribution area of shared bikes. Furthermore, a new matrix of riding pattern based on POI was proposed to identify the riding patterns during massive sharing bicycle dataset.</p><p>This paper aims to explore the riding behaviour of shared bicycles, and the research results are as follows:</p><p>(1) Temporal characteristics of riding behaviour</p><p>The use of the Mobike bicycles is significantly different on weekdays and weekends (Figure1). Figure 2 clearly shows a morning peak (7&amp;ndash;9&amp;thinsp;h) and evening peak (17&amp;ndash;19&amp;thinsp;h), corresponding with typical commute time. At noon, some users' dining activities triggered a certain close-distance riding behavior, which formed a noon peak. Different from the riding characteristics of the working days, there are many recreational and leisure riding behaviors on the weekends. The distribution of riding time is more balanced, and there is no obvious morning and evening peak phenomenon.</p><p>(2) Spatial characteristics of riding behavior</p><p> The spatial distribution of riding behaviour varies with different roads (Figure 2) and people prefer to choose trunk roads for cycling trips. Spatial hotpot detecting method based on the kernel density is applied to identify the active degree of bike sharing trip during a whole weekday (Figure 3). The red colour represents a high active degree and the green and blue colour means the low degree. Note that almost no riding occurred in the early hours of the morning and late at night. The characteristics of three riding peaks are obvious in the figure. A large number of travels occurred in Second Ring to Fourth Ring Road, and some travel activities were concentrated near traffic sites.</p><p>(3) Patterns of riding behavior</p><p> Different riding patterns happens in different space and change over the time at two scales of day and hour. During morning peak and evening peak on weekdays, more than 60 percent of riding trips are corresponding with typical commuting activities. The observed commuting pattern of morning peak (Figure 4(a) and (b)) implies that the majority of shared bicycle trips might relate to home, transports, commercial area and some institution. For example, students choose shared bicycles to do some school activities, people prefer to use shared bicycles as a connection tool to bus station and metro stops and people handle daily affairs in some government agencies. However, a large part of the shared bicycle trips on weekends shows the characteristics of non-commuting riding pattern, which means more leisure activities take place at weekends (Figure 4(c) and (d)). Non-commuting pattern of riding behavior mainly occurs among residential areas, metro stops, bus stations and recreational facilities, such as parks, playgrounds, etc.</p>

scholarly journals Uncertainty Analysis of Multisource Land Cover Products in China

2021 ◽  
Vol 13 (16) ◽  
pp. 8857
Author(s):  
Longhao Wang ◽  
Jiaxin Jin
Keyword(s):  
Land Cover ◽  
Forest Cover ◽  
Regional Scale ◽  
Least Squares Regression ◽  
Average Deviation ◽  
Deviation Analysis ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Forest Coverage ◽  
High Consistency

Satellite-based land cover products play a crucial role in sustainability. There are several types of land cover products, such as qualitative products with discrete classes, semiquantitative products with several classes at a predetermined ratio, and quantitative products with land cover fractions. The proportions of land cover types in the grids with coarse resolution should be considered when used at the regional scale (e.g., modeling and remote sensing inversion). However, uncertainty, which varies with spatial distribution and resolution, needs to be studied further. This study used MCD12, ESA CCI, and MEaSURES VCF land cover data as indicators of qualitative, semiquantitative, and quantitative products, respectively, to explore the uncertainty of multisource land cover data. The methods of maximum area aggregation, deviation analysis, and least squares regression were used to investigate spatiotemporal changes in forests and nontree vegetation at diverse pixel resolutions across China. The results showed that the average difference in forest coverage for the three products was 8%, and the average deviation was 11.2%. For forest cover, the VCF and ESA CCI exhibited high consistency. For nontree vegetation, the ESA CCI and MODIS exhibited the lowest differences. The overall uncertainty in the temporal and spatial changes of the three products was relatively small, but there were significant differences in local areas (e.g., southeastern hills). Notably, as the spatial resolution decreased, the three products’ uncertainty decreased, and the resolution of 0.1° was the inflection point of consistency.

scholarly journals Distribution update, conservation status and color in life of the rare Amazonian snake, Eutrachelophis papilio Zaher and Prudente 2019 (Serpentes: Xenodontini)

2020 ◽  
Vol 50 (3) ◽  
pp. 252-255
Author(s):  
Nathalie CITELI ◽  
Mariana DE-CARVALHO ◽  
Reuber BRANDÃO
Keyword(s):  
Convex Polygon ◽  
Conservation Status ◽  
Biological Data ◽  
Distribution Area ◽  
Minimum Convex Polygon ◽  
Human Footprint ◽  
Data Deficient ◽  
First Time ◽  
Polygon Method ◽  
Anthropic Pressure

ABSTRACT The rare Amazonian snake Eutrachelophis papilio is known from only five individuals, from four localities, belonging to its type-series, the more recent collected over 10 years ago. Here, we expand its distribution and describe its color in life for the first time. We also provide an estimate of its distribution area using the minimum convex polygon method and identify the values of anthropic pressure within its known distribution range with the Human Footprint Index. The new occurrence is located 291 km from the nearest known locality and its distribution is associated with pristine forests. Considering its rarity, and the absence of demographic and biological data, we suggest that the species should be classified as Data Deficient by IUCN criteria.

Chara kirghisorum (Charales): lectotypification, first reliable record in Europe and update of species distribution in Russia

Phytotaxa ◽  
2018 ◽  
Vol 362 (2) ◽  
pp. 220 ◽  
Author(s):  
ROMAN E. ROMANOV ◽  
SHAMIL R. ABDULLIN
Keyword(s):  
Species Distribution ◽  
Distribution Range ◽  
Orenburg Oblast

Charophytes are commonly easily recognizable plants irrespective of their size due to the recurrent combination of very similar parts within their thalli. A few species are outstanding due to their unusual whip-like appearance with regularly distributed small nodules on the stems formed with whorls of short and nearly rudimentary branchlets. The central and north-European Chara filiformis A. Braun in Hertzsch (1855: 81) and mainly Middle-Asian C. kirghisorum Lessing (1834: 212) are examples of this distinctive habit. The species differ essentially in gametangia arrangement as well as in their distribution range. Monoecious C. filiformis does not have reliable records east of Pskov Oblast of Russia (29° E), while dioecious C. kirghisorum is unknown west of Orenburg Oblast of Russia (58° E) (Hollerbach 1941; Hollerbach & Krassavina 1983; LE!, PSK!). Therefore, they may be recognized as vicariate species.

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