scholarly journals Effects of environmental variables on seedling distribution of rare and endangered Dacrydium pierrei

2017 ◽  
Vol 12 (1) ◽  
pp. 345-355
Author(s):  
Chunyan Wu ◽  
Yongfu Chen ◽  
Qiao Chen ◽  
Wenquan Wang ◽  
Xiaojiang Hong ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Environmental Variables ◽  
Seedling Emergence ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Available Phosphorus ◽  
Strong Impact ◽  
Growth Environment ◽  
Rare And Endangered

AbstractBecause growth environment is affected by climate change, Dacrydium pierrei resources are becoming less and less. Therefore, understanding the effects of environmental variables on seedling-sapling distributions can help gain insight into changes in population recruitment in the context of climate change. The seedling-saplings distribution and variability of Dacrydium pierrei in environmental variables at Bawangling, Hainan, China, was surveyed over a 3-year period. In addition, laboratory experiments measuring the effects of soil moisture on seedling emergence were conducted to identify seedling development characteristics; principal component analysis (PCA) and Gaussian mixture model (GMM) were used to assess how different factors influenced Dacrydium pierrei seedlings-saplings distribution. The results demonstrated that the influence degree of seedling-sapling distribution is soil temperature>litter thickness>available phosphorus>canopy density> available potassium>nitrate nitrogen; a large number of seedling-saplings occurring at altitudes 1140-1300 m; a GMM trained with a C2-L3-H4-A5-I6 combination yielded an accuracy of 72.23% in simulating seedling-saplings distribution; temperature and precipitation have strong impact on seedling-sapling distribution, with increasing soil moisture, seedling emergence shows a positive relationship. This study focuses more on developing a new method for research on the seedling-sapling distribution of Dacrydium pierrei to get reference for its adaptive management with the intense extreme climate change.

scholarly journals Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome

2017 ◽  
Author(s):  
Alexandra Asaro ◽  
Brian P. Dilkes ◽  
Ivan Baxter
Keyword(s):  
Environmental Variables ◽  
Physiological State ◽  
Principal Component ◽  
Interactive Effects ◽  
Single Element ◽  
Integrated Network ◽  
Growth Environment ◽  
Maize Kernel ◽  
Full Complement ◽  
Element Accumulation

AbstractPlants obtain elements from the soil through genetic and biochemical pathways responsive to physiological state and environment. Most perturbations affect multiple elements which leads the ionome, the full complement of mineral nutrients in an organism, to vary as an integrated network rather than a set of distinct single elements. To examine the genetic basis of covariation in the accumulation of multiple elements, we analyzed maize kernel ionomes from Intermated B73 × Mo17 (IBM) recombinant inbred populations grown in 10 environments. We compared quantitative trait loci (QTL) determining single-element variation to QTL that predict variation in principal components (PCs) of multiple-element covariance. Single-element and multivariate approaches detected partially overlapping sets of loci. In addition to loci co-localizing with single-element QTL, multivariate traits within environments were controlled by loci with significant multi-element effects not detectable using single-element traits. Gene-by-environment interactions underlying multiple-element covariance were identified through QTL analyses of principal component models of ionome variation. In addition to interactive effects, growth environment had a profound effect on the elemental profiles and multi-element phenotypes were significantly correlated with specific environmental variables.Author SummaryA multivariate approach to the analysis of element accumulation in the maize kernel shows that elements are not regulated independently. By describing relationships between element accumulation we identified new genetic loci invisible to single-element approaches. The mathematical combinations of elements distinguish groups of plants based on environment, demonstrating that observed variation derives from interactions between genetically controlled factors and environmental variables. These results suggest that successful application of ionomics to improve human nutrition and plant productivity requires simultaneous consideration of multiple-element effects and variation of such effects in response to environment.

scholarly journals Erosion Resistance and Fertility of Frost-Resistant Ecological Substrate in Alpine Region

2021 ◽  
Vol 20 (4) ◽  
Author(s):  
Cailing Xue ◽  
Ailinaizaier Ainiwaer ◽  
Jiazhen Gao ◽  
Zhaohui Qin
Keyword(s):  
Organic Matter ◽  
Rock Slope ◽  
Erosion Resistance ◽  
Principal Component ◽  
Alpine Region ◽  
Natural Soil ◽  
Available Phosphorus ◽  
Average Weight ◽  
Available Nitrogen ◽  
Growth Environment

This research was conducted to quantitatively evaluate the application effect of the frost-resistant ecological substrate in the rock slope of the hydropower station. Field sampling and laboratory tests were conducted to determine the erosion resistance and fertility of frost-resistant ecological substrate, and the test results were compared with those of natural soils with similar site conditions. The research conclusions were as follows. Compared with the natural soil, the content of > 0.25 mm mechanical-stable aggregates, > 0.25 mm water-stable aggregates, average weight diameter, geometric average diameter, organic matter, available nitrogen, available phosphorus, and available potassium of frost-resistant ecological substrate, significantly increased. On the contrary, erodibility factor, percentage aggregate disruption, aggregate degree, and dispersion rate decreased evidently. These results showed that erosion resistance and fertility of the frost-resistant ecological substrate have a better prospect in the engineering application of alpine regions. In addition, the principal component analysis showed that the principal component value of frost-resistant ecological substrate increased by 1.9 times that of natural soil. According to the correlation study, the increase in the amount of > 0.25 mm macro-aggregates and organic matter is the primary reason that ecological substrate has greater stability and fertility than natural soil. In conclusion, the frost-resistant ecological substrate was a suitable soil to create a suitable vegetation growth environment on the surface of rock slope in the alpine region.

scholarly journals Using climatic variables alone overestimate climate change impacts on predicting distribution of an endemic species

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256918
Author(s):  
Somayeh Zangiabadi ◽  
Hassan Zaremaivan ◽  
LIuis Brotons ◽  
Hossein Mostafavi ◽  
Hojjatollah Ranjbar
Keyword(s):  
Climate Change ◽  
Plant Species ◽  
Species Distribution ◽  
Environmental Variables ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Principal Component ◽  
Distribution Range ◽  
Climatic Variables ◽  
Prediction Ability

Plant species distribution is constrained by both dynamic and static environmental variables. However, relative contribution of dynamic and static variables in determining species distributions is not clear and has far reaching implications for range change dynamics in a changing world. Prunus eburnea (Spach) Aitch. & Hemsl. is an endemic and medicinal plant species of Iran. It has rendered itself as ecologically important for its functions and services and is currently in need of habitat conservation measures requiring investigation of future potential distribution range. We conducted sampling of 500 points that cover most of Iran plateau and recorded the P. eburnea presence and absence during the period 2015–2017. In this study, we evaluated impacts of using only climatic variables versus combined with topographic and edaphic variables on accuracy criteria and predictive ability of current and future habitat suitability of this species under climate change (CCSM4, RCP 2.6 in 2070) by generalized linear model and generalized boosted model. Models’ performances were evaluated using area under the curve, sensitivity, specificity and the true skill statistic. Then, we evaluated here, driving environmental variables determining the distribution of P. eburnea by using principal component analysis and partitioning methods. Our results indicated that prediction with high accuracy of the spatial distribution of P. eburnea requires both climate information, as dynamic primary factors, but also detailed information on soil and topography variables, as static factors. The results emphasized that environmental variable grouping influenced the modelling prediction ability strongly and the use of only climate variables would exaggerate the predicted distribution range under climate change. Results supported using both dynamic and static variables improved accuracy of the modeling and provided more realistic prediction of species distribution under influence of climate change.

scholarly journals Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Nabaz R. Khwarahm
Keyword(s):  
Climate Change ◽  
Habitat Suitability ◽  
Environmental Variables ◽  
Climate Changes ◽  
Future Climate ◽  
Annual Precipitation ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Kurdistan Region ◽  
Oak Tree

Abstract Background The oak tree (Quercus aegilops) comprises ~ 70% of the oak forests in the Kurdistan Region of Iraq (KRI). Besides its ecological importance as the residence for various endemic and migratory species, Q. aegilops forest also has socio-economic values—for example, as fodder for livestock, building material, medicine, charcoal, and firewood. In the KRI, Q. aegilops has been degrading due to anthropogenic threats (e.g., shifting cultivation, land use/land cover changes, civil war, and inadequate forest management policy) and these threats could increase as climate changes. In the KRI and Iraq as a whole, information on current and potential future geographical distributions of Q. aegilops is minimal or not existent. The objectives of this study were to (i) predict the current and future habitat suitability distributions of the species in relation to environmental variables and future climate change scenarios (Representative Concentration Pathway (RCP) 2.6 2070 and RCP8.5 2070); and (ii) determine the most important environmental variables controlling the distribution of the species in the KRI. The objectives were achieved by using the MaxEnt (maximum entropy) algorithm, available records of Q. aegilops, and environmental variables. Results The model demonstrated that, under the RCP2.6 2070 and RCP8.5 2070 climate change scenarios, the distribution ranges of Q. aegilops would be reduced by 3.6% (1849.7 km2) and 3.16% (1627.1 km2), respectively. By contrast, the species ranges would expand by 1.5% (777.0 km2) and 1.7% (848.0 km2), respectively. The distribution of the species was mainly controlled by annual precipitation. Under future climate change scenarios, the centroid of the distribution would shift toward higher altitudes. Conclusions The results suggest (i) a significant suitable habitat range of the species will be lost in the KRI due to climate change by 2070 and (ii) the preference of the species for cooler areas (high altitude) with high annual precipitation. Conservation actions should focus on the mountainous areas (e.g., by establishment of national parks and protected areas) of the KRI as climate changes. These findings provide useful benchmarking guidance for the future investigation of the ecology of the oak forest, and the categorical current and potential habitat suitability maps can effectively be used to improve biodiversity conservation plans and management actions in the KRI and Iraq as a whole.

scholarly journals Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation

2021 ◽  
Vol 13 (2) ◽  
pp. 223
Author(s):  
Zhenyang Hui ◽  
Shuanggen Jin ◽  
Dajun Li ◽  
Yao Yevenyo Ziggah ◽  
Bo Liu
Keyword(s):  
Transfer Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Principal Component ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Individual Tree ◽  
Terrestrial Lidar ◽  
Initial Segmentation ◽  
Tree Extraction

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

scholarly journals An Automated Pipeline for Dynamic Detection of Sub-Surface Metal Loss Defects across Cold Thermography Images

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4811
Author(s):  
Siavash Doshvarpassand ◽  
Xiangyu Wang
Keyword(s):  
Test Piece ◽  
Surface Defects ◽  
Principal Component ◽  
Specific Area ◽  
Gaussian Mixture ◽  
Metal Loss ◽  
Test Surface ◽  
Thermal Excitation ◽  
Cold Stimulation ◽  
Surface Metal

Utilising cooling stimulation as a thermal excitation means has demonstrated profound capabilities of detecting sub-surface metal loss using thermography. Previously, a prototype mechanism was introduced which accommodates a thermal camera and cooling source and operates in a reciprocating motion scanning the test piece while cold stimulation is in operation. Immediately after that, the camera registers the thermal evolution. However, thermal reflections, non-uniform stimulation and lateral heat diffusions will remain as undesirable phenomena preventing the effective observation of sub-surface defects. This becomes more challenging when there is no prior knowledge of the non-defective area in order to effectively distinguish between defective and non-defective areas. In this work, the previously automated acquisition and processing pipeline is re-designed and optimised for two purposes: 1—Through the previous work, the mentioned pipeline was used to analyse a specific area of the test piece surface in order to reconstruct the reference area and identify defects. In order to expand the application of this device over the entire test area, regardless of its extension, the pipeline is improved in which the final surface image is reconstructed by taking into account multiple segments of the test surface. The previously introduced pre-processing method of Dynamic Reference Reconstruction (DRR) is enhanced by using a more rigorous thresholding procedure. Principal Component Analysis (PCA) is then used in order for feature (DRR images) reduction. 2—The results of PCA on multiple segment images of the test surface revealed different ranges of intensities across each segment image. This potentially could cause mistaken interpretation of the defective and non-defective areas. An automated segmentation method based on Gaussian Mixture Model (GMM) is used to assist the expert user in more effective detection of the defective areas when the non-defective areas are uniformly characterised as background. The final results of GMM have shown not only the capability of accurately detecting subsurface metal loss as low as 37.5% but also the successful detection of defects that were either unidentifiable or invisible in either the original thermal images or their PCA transformed results.

scholarly journals Historical climate controls soil respiration responses to current soil moisture

2017 ◽  
Vol 114 (24) ◽  
pp. 6322-6327 ◽  
Author(s):  
Christine V. Hawkes ◽  
Bonnie G. Waring ◽  
Jennifer D. Rocca ◽  
Stephanie N. Kivlin
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Soil Respiration ◽  
Carbon Cycling ◽  
Large Scale ◽  
Microbial Respiration ◽  
Rainfall Gradient ◽  
Soil Microbial ◽  
Historical Climate ◽  
Moisture Dependence

Ecosystem carbon losses from soil microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40–70 Pg carbon from soil to the atmosphere each year. Because these microbial processes can feed back to climate change, understanding respiration responses to environmental factors is necessary for improved projections. We focus on respiration responses to soil moisture, which remain unresolved in ecosystem models. A common assumption of large-scale models is that soil microorganisms respond to moisture in the same way, regardless of location or climate. Here, we show that soil respiration is constrained by historical climate. We find that historical rainfall controls both the moisture dependence and sensitivity of respiration. Moisture sensitivity, defined as the slope of respiration vs. moisture, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter soils compared with historically drier soils. The respiration–moisture relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall.

Assess 21st century Flash Drought in the United States using high resolution regional climate models

2021 ◽  
Author(s):  
Brandi Gamelin ◽  
Jiali Wang ◽  
V. Rao Kotamarthi
Keyword(s):  
Climate Change ◽  
United States ◽  
Soil Moisture ◽  
Climate Models ◽  
Wrf Model ◽  
The United States ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Regional Variability ◽  
Groundwater Levels

<p>Flash droughts are the rapid intensification of drought conditions generally associated with increased temperatures and decreased precipitation on short time scales.  Consequently, flash droughts are responsible for reduced soil moisture which contributes to diminished agricultural yields and lower groundwater levels. Drought management, especially flash drought in the United States is vital to address the human and economic impact of crop loss, diminished water resources and increased wildfire risk. In previous research, climate change scenarios show increased growing season (i.e. frost-free days) and drying in soil moisture over most of the United States by 2100. Understanding projected flash drought is important to assess regional variability, frequency and intensity of flash droughts under future climate change scenarios. Data for this work was produced with the Weather Research and Forecasting (WRF) model. Initial and boundary conditions for the model were supplied by CCSM4, GFDL-ESM2G, and HadGEM2-ES and based on the 8.5 Representative Concentration Pathway (RCP8.5). The WRF model was downscaled to a 12 km spatial resolution for three climate time frames: 1995-2004 (Historical), 2045-2054 (Mid), and 2085-2094 (Late).  A key characteristic of flash drought is the rapid onset and intensification of dry conditions. For this, we identify onset with vapor pressure deficit during each time frame. Known flash drought cases during the Historical run are identified and compared to flash droughts in the Mid and Late 21<sup>st</sup> century.</p>

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