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Relationships of soil means and heterogeneity with ecoregional plant diversity across three vulnerability degrees. Ecoregional plant diversity refers to vascular plant species richness organized by ecoregion.
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Environmental indicators have been developed widely to promote biodiversity conservation, ecological restoration, and nature resource management from local to global scales. Ecoregions are effective tools for global conservation of plant diversity, and soil conditions can affect the plant diversity within ecoregions. Hence, soil indicators of plant...
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Context 1
... and EPD across the three vulnerability categories indicating the predictive value of soil means was stronger than that of soil heterogeneity for EPD (Fig. 2). As cation exchange capacity mean and soil organic carbon stock heterogeneity increased, EPD deceased initially and then increased in critical or endangered ecoregions (P < 0.05; Table 2 and Fig. 3). Means of soil texture clay fraction and bulk density heterogeneity were positively related to EPD for the vulnerable and relatively stable or intact ecoregions (P < 0.05; Table 2 and Fig. ...
Context 2
... and soil organic carbon stock heterogeneity increased, EPD deceased initially and then increased in critical or endangered ecoregions (P < 0.05; Table 2 and Fig. 3). Means of soil texture clay fraction and bulk density heterogeneity were positively related to EPD for the vulnerable and relatively stable or intact ecoregions (P < 0.05; Table 2 and Fig. ...
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... Understanding the distribution of diversity in agricultural landscapes is of utmost importance for designing sound strategies that promote environmental restoration and conservation (Gavish et al., 2019). Landscapes may provide different conditions for diverse taxonomic groups at different spatial scales (Weibull et al., 2003), and both local and landscape-level variables can be used to monitor the diversity of plant species, thus supporting effective initiatives to promote the conservation of biodiversity and ecological restoration within agricultural landscapes (Wan et al., 2018). In addition to abiotic characteristics, the success of restoration strategies focused on the conservation of biodiversity depends on landscape characteristics (Leite et al., 2013;Matthews et al., 2009). ...
The biodiversity in agricultural landscapes depends on land‐use types, natural elements, and farming intensity. The Brazilian Cerrado is a biodiversity hotspot, however, the native vegetation loss rates have increased in the last years, leading several plant species to extinction risk. Here, we analyze the effects of local (soil fertility and litter amount) and landscape variables (patch shape, habitat amount, compositional heterogeneity, matrix dominance and connectivity) on alpha species richness and diversity, and basal area of different Cerrado vegetation types (savanna, forested savanna, and forest) using linear models and Akaike information criterion. We calculated landscape metrics at different spatial scales for 49 sampling sites in intensive farming landscapes. Woody plant diversity and richness were positively related to soil fertility and landscape compositional heterogeneity at 1500 m spatial scale. Plant richness was negatively related to the patch shape of remnants at 1000 m spatial scale. The effects of litter amount to explain the basal area varied between vegetation types. We found a positive effect in savanna and forested savanna and a negative effect in forest areas. Our findings suggest that the increase of monocultures lead to a decline on plant diversity in the studied landscapes. Also, local variables and the diversity of vegetation types need to be considered to design restoration actions in Cerrado. Sustainable intensification and biodiversity‐friendly production systems may be alternatives to increase compositional heterogeneity, thus favoring plant diversity in Cerrado farming landscapes.
Abstract in Portuguese is available with online material.
... The importance of CEC in assessing soil quality and its potential for various land management systems under different soil types and climate conditions is broadly accepted (Aparicio & Costa, 2007;Biswas et al., 2017;Chaves et al., 2017;Marinari et al., 2006;Masto et al., 2009;Mukhopadhyay et al., 2019;Pulido et al., 2017;Wan et al., 2018;Zuber et al., 2017). Knowledge of CEC is becoming increasingly important in dry areas because they suffer from many problems such as a lack of sufficient water to support plant growth and land degradation. ...
Cation exchange capacity (CEC) is an important soil property because it affects the assimilation of nutrients and buffers against soil acidification. Thus, knowledge of CEC is considered key to developing agricultural and environmental models for land management planning. However, in developing countries such as Sudan, there is a lack of soil CEC data due to the absence of research projects and funding to develop this information. Therefore, this research was conducted to predict CEC for large areas using specific soil physical characteristics, including soil texture and saturation percentage (SP), for which there is potentially available data. To achieve this goal, the properties of 430 soil samples (301 for training and 129 for validation) were obtained from the soil database of the Soil Survey Administration, Ministry of Agriculture, Sudan, which had different soil depth intervals (0–0.3 m, 0.3–0.6 m, 0.6–0.9 m, 0.9–1.5 m, and >1.5 m) from Entisols in the Northern State of Sudan. The data were stratified into homogeneous groups based on the textural classes of the main soil order. Then, regression models were performed and evaluated using the coefficient of determination (R2), standard error of the estimate (SEE), and root mean square error (RMSE). The results indicated that in individual Entisols and textural classes, the combined soil covariates silt, clay, and SP were the best properties to predict CEC values (R2 ranged from 0.86 to 0.99). The regression models did not provide statistically significant results for the silty clay loam textural class (R2 ranged from 0.01 and 0.35). The findings of this modeling study could be applied to other Entisols worldwide with divergent textural classes, which could be used to verify the suggested CEC pedotransfer functions and/or improve them. This would help farmers correctly design soil management plans and prevent acidification issues if combined with other soil properties data.
... Ecoregions cover relatively large areas of land and, in their natural state, include characteristic, geographically distinct features of plant diversity (Olson et al., 2001;Wang and Wan, 2018). Many studies (Kier et al., 2005;van Vuuren et al., 2006;Blarquez et al., 2014;Wan et al., 2018a; have explored mechanisms of geographical influence on ecoregional plant diversity (EPD). Climate change may increase plant species richness across different temporal and spatial scales (Suggitt et al., 2019), and Kier et al. (2005) have indicated that human influence may be the driver of plant diversity in terrestrial ecoregions at large scale due to the loss and degradation of natural habitats and ecosystems. ...
... However, such filtering effects may be extremely weak at large scales (e.g., ecoregions) compared to small scales (i.e., communities; Kraft et al., 2015;Stark et al., 2017;Harrison et al., 2020). Plant diversity is determined by environmental filtering of the regional flora; local resource heterogeneity, resource partitioning, nutrient stoichiometry, or soil fertility may play small roles in realized patterns of plant diversity (Wang et al., 2009;Laliberté et al., 2014;Xu et al., 2018;Wan et al., 2018a;. However, variation in climates may affect EPD within any specific ecoregion. ...
Large-scale patterns of plant diversity and their determinants are central issues in macroecology and biodiversity conservation. Previous studies have shown that late Quaternary and contemporary climates can affect plant diversity at large scale. The main objective of our study was to explore the effects of late Quaternary and contemporary climates on ecoregional plant diversity (EPD). Here, we used global ecoregions to examine characteristic and geographically distinct features of plant diversity and quantified the joint and independent explanatory power of late Quaternary and contemporary climates to account for EPD across different biomes. Then, we tested environmental mean and heterogeneity hypotheses and found that both late Quaternary and contemporary climates (temperature and precipitation) can affect plant diversity at ecoregional scales. Furthermore, environmental heterogeneity (i.e., the heterogeneity of late Quaternary and contemporary climates) may have stronger explanatory power than environmental mean for EPD. However, climatic effects on EPD may depend on biome variation. The effects of late Quaternary and contemporary climates on EPD can persist widely in forest biomes and Temperate Grasslands, Savannas & Shrublands. The results of the current study inform the prediction of plant diversity under future climate change, and support long-term monitoring of plant diversity at ecoregional scales. © 2021, Pakistan Agricultural Scientists Forum. All rights reserved.
... The classification process will present more reliable results when aspects associated with natural environments (climate, soil, relief, water, etc.) and anthropic environments (roads, constructions, urban area) begin to be considered (Perez Filho, 2007), as they determine and guide LULC. Several studies (Potter et al., 2003;Ter Steege et al., 2006;Arruda et al., 2017;Wan et al., 2018Wan et al., , 2019 have demonstrated that, although vegetation is a result of climatic conditions and the species availability associated with the local biogeography, soil properties and relief characteristics should also be considered as conditioning variables of the distribution and type of vegetation. In order to improve the distinction between the classes of LULC, the incorporation of environmental information into the spectral data presents itself as an alternative for recognizing patterns of occurrence of the classes of LULC, when there is limitation for spectral discrimination (Duguay et al., 1989;Franklin, 1989;Franklin and Wilson, 1991;Franklin, 1995;Harris and Ventura, 1995;Bruzzone et al., 1997;Ricchetti, 2000;Rogan et al., 2002;Zukowskyj et al., 2003;Lu and Weng, 2007;Li, 2010;Lu et al. (2012)a, b), especially in complex biophysical environments with different species (Lu et al. (2012)a, b), such as areas of ecotone. ...
... Similarly, the Arenosol variable (coarse textured soils that have a low water holding capacity, rapid permeability, low nutrient content, and little vegetation cover) was selected because of its relationship with the spatialization of the forest covers present in the study area: Dense Tropical Forest and Cerrado Sensu Stricto, the former two of which are grouped in the Evergreen Forest class. Potter et al. (2003);Ter Steege et al. (2006); Wan et al. (2018) and Wan et al. (2019) state that factors related to soil type may affect the spatial patterns of vegetation diversity and distribution. ...
Optical image classification converts spectral data into thematic information from the spectral signature of each object in the image. However, spectral separability is influenced by intrinsic characteristics of the targets, as well as the characteristics of the images used. The classification process will present more reliable results when aspects associated with natural environments (climate, soil, relief, water, etc.) and anthropic environments (roads, constructions, urban area) begin to be considered, as they determine and guide land use and land cover (LULC). The objectives of this study are to evaluate the integration of environmental variables with spectral variables and the performance of the Random Forest algorithm in the classification of Landsat-8 OLI images, of a watershed in the Eastern Amazon, Brazil. The classification process used 96 predictive variables, involving spectral, geological, pedological, climatic and topographic data and Euclidean distances. The selection of variables to construct the predictive models was divided into two approaches: (i) data set containing only spectral variables, and (ii) set of environmental variables added to the spectral data. The variables were selected through nonlinear correlation analysis, with the Randomized Dependence Coefficient and the Recursive Feature Elimination (RFE) method, using the Random Forest classifier algorithm. The spectral variables NDVI, bands 2, 4, 5, 6 and 7 of the dry season and band 4 of the rainy season were selected in both approaches (i and ii). The Euclidean distance from the urban area, Arenosol soil class, annual precipitation, precipitation in February and precipitation of the wettest quarter were the variables selected from the auxiliary data set. This study showed that the addition of environmental data to the spectral data reduces the limitation of the latter, regarding the discrimination of the different classes of LULC, in addition to improving the accuracy of the classification. The addition of soil classes to spectral variables provided a reduction in errors for vegetation classification (Evergreen Forest and Cerrado Sensu Stricto), as it was able to inform about nutrient availability and water storage capacity. The study demonstrates that the addition of environmental variables to the spectral variables can be an alternative to improve monitoring in areas of ecotone in Neotropical regions.
... Such drivers include the size of wild populations, fragmentation of their habitat, and biological invasion, which may further exacerbate the negative effects of climate change on VPR (Stockwell et al., 2003;Opdam and Wascher, 2004;Thuiller et al., 2005;Didham et al., 2007;Urban, 2015;Feng et al., 2016). The characteristics of the soil of a particular habitat are also important predictors of VPR at large spatial scales (Bedford et al., 1999;Cousins and Eriksson, 2001;Goldblatt and Manning, 2002;Wan et al., 2018aWan et al., , 2018b. For example, Wan et al. (2018b) identified the mean values of soil texture, clay fraction, and heterogeneity of bulk density as useful predictors of VPR from intact to vulnerable ecoregions. ...
... The characteristics of the soil of a particular habitat are also important predictors of VPR at large spatial scales (Bedford et al., 1999;Cousins and Eriksson, 2001;Goldblatt and Manning, 2002;Wan et al., 2018aWan et al., , 2018b. For example, Wan et al. (2018b) identified the mean values of soil texture, clay fraction, and heterogeneity of bulk density as useful predictors of VPR from intact to vulnerable ecoregions. VPR can also be affected by human activity. ...
... Mean annual temperature, minimum temperature of the coldest month, and annual precipitation largely predict VPR across taxonomic levels, indicating that records of temperature and precipitation can be used as environmental predictors of VPR. Numerous studies (e.g., O'Brien et al., 1998;Iverson and Prasad, 2001;Thuiller et al., 2005;Feng et al., 2016;Wan et al., 2018b) showed that changes in temperature and precipitation affect the distribution patterns of VPR at large spatial scales, thus facilitating the development of models for the prediction of broad-scale VPR for environmental monitoring and biodiversity conservation. The relationships between species richness and climate across different taxonomic groups of vascular plants are consistent, mainly because distribution patterns of water and energy resources can explain the variation of plant species richness at large spatial scales (O'Brien et al., 1998;Lundholm, 2009;Wang et al., 2010). ...
Vascular plant richness contributes significantly to ecosystem function and services. Understanding the environmental predictors of vascular plant richness can therefore support conservation efforts of biodiversity and ecosystem services at large spatial scales. This study used protected area data to explore the environmental predictors of vascular plant richness at different taxonomic levels (i.e., family, genus, and species), to improve the integrity and function of protected areas. Climate variables (i.e., annual mean temperature, minimum temperature of the coldest month, and annual precipitation) and soil parameters (i.e., soil texture, soil pH, bulk density, organic carbon stock, and coarse-fragment volume) were found to largely predict vascular plant richness across different taxonomic levels. Moreover, these were the most effective at the family level. The following measures are therefore proposed to improve conservation management of protected areas. 1) The effects of climate change on vascular plant richness should be accounted for during the spatial planning of protected areas. 2) The interaction between vascular plant richness and soil variables should be considered for the management of protected areas. 3) Human activities should be controlled to establish a balance between strict protection and the sustainable use of natural resources in the protected area network. However, the effect of human activities alone could not explain the variance of vascular plant richness at large spatial scales. This study provides effective guidelines for the sustainable management of plant diversity in protected areas at large spatial scales.
... SOC, and cation-exchange capacity (CEC) as well as soil total N (TN), available P (AP), and available K (AK) are crucial for carbon sequestration, soil fertility, and vegetation recovery (Ruiz-Navarro et al., 2009;Qiu et al., 2018). Other physicochemical soil parameters such as pH, bulk density (BD), and the C:N ratio may also have a key role in the soil functionality and the restoration of degraded lands and biodiversity (Wan et al., 2018;Zhang et al., 2018). Finally, available water-holding capacity (AWHC) contributes to several ecosystem services such as water purification and biomass production (Costantini et al., 2016). ...
... For example, soil water dynamics could shape different forest vegetation covers (Hayati et al., 2018;Molina et al., 2019). Furthermore, some studies (e.g., Potter et al., 2003;Ter Steege et al., 2006;Wan et al., 2018a) showed that soil factors can affect the spatial patterns of plant diversity on a large scale. However, only few studies have shown the relationships between soil variables and the spatial distribution of forest vegetation on a large spatial scale. ...
... We used ArcGIS 10.6 (https://www.esri.com/en-us/home) to transfer the resolution of soil and human footprint data from 0.5 arc-minutes to 10.0 arc-minutes. These environmental variables were selected because they potentially influence the distribution of forest vegetation (Hou, 201;Kier et al., 2005;Chen et al., 2015;Wang et al., 2017;Wan et al., 2018a and 2018b). ...
... Furthermore, plant diversity shows a narrow thermal tolerance to climatic changes and low dispersal on a large spatial scale (García-Robledo et al., 2016;Bush et al., 2018;Polato et al., 2018). The features of climatic tolerance and dispersal ability are extremely vulnerable to climate change (Bush et al., 2018;Polato et al., 2018;Wan et al., 2018a). For example, Zhou et al. (2013) showed that climate variance (e.g., global warming and drought stress) could lead to predicted reductions in rainfall and forest productivity, increased tree mortality and declining forest biomass carbon sinks in a subtropical, monsoon, evergreen broad-leaved forest in Southern China. ...
The investigation of factors affecting the spatial distribution of forest vegetation on a large scale is a hot topic in forestry and ecology. Numerous studies have reported that climate and human activities have a considerable effect on the spatial distribution of forest vegetation. However, few studies have focused on the effects of soil properties on the spatial distribution of forest vegetation across China. In the present study two indicators were used to explore such soil effects, namely the percentage contribution of soil properties to the spatial distribution of forest vegetation and the similarity of the potential distributions based on climate variables and both climate and soil variables under distribution modelling. We found that 1)soil parameters (e.g., coarse fragment volume and organic carbon stock)contribute to the spatial distribution of forest vegetation in China, although this contribution may vary among different biomes and vegetation classes and 2)the spatial distribution of forest vegetation differs among the different vegetation classes and biomes. Furthermore, soil variables (e.g., coarse fragment volume and organic carbon stock)could play an important role in the spatial distribution of conifer-broadleaf forest vegetation and mountain broad-leaved and conifer-leaved forest vegetation. However, climate variables were more important than soil properties across most vegetation types. It is therefore suggested that 1)coarse fragment volume and organic carbon stock should be used as indicators to monitor forest vegetation and 2)soil properties should be conserved to facilitate reforestation programs in China. The present study provides evidence that soil parameters affect the spatial distribution of forest vegetation in China, facilitating the development of effective management strategies.
The technology of using the indicative properties of plants both for obtaining scientific results and teaching this technique to students and young scientists is proposed for implementation. Aspects of research organization and didactic work in the implementation of the environmental monitoring program of Donbass are highlighted. Over the period of research (1996-2023), high levels of pollution and anthropogenic transformation of ecosystems in the industrial areas of Eastern Europe have been established. The data are based both on experiments in open landscapes and laboratory conditions, special procedures for statistical processing and interpretation of the results. These localities of intensive economic use are the places of great scientific and applied interest to ecologists and educators in this area. Indicator plants are visual objects in the knowledge of the fundamental nature and practical use for information about the quality of the environment. In scientific and educational activities the following are important: organization of a laboratory, availability of equipment, functioning of a museum, a card-index, a herbarium fund, the possibility of introducing case studies technology, the theory of solving inventive tasks, conducting interactive lectures, seminars, demonstration experiments and special modern technologies for training environmental specialists – modeling and land-use forecasting and urbanized environment. Methods of implementation of scientific and pedagogical experiment on phytomonitoring and ecological expertise with the help of plants have been introduced into work with students, postgraduates and young scientists of the Department of Botany and Ecology of Donetsk State University.
Grazing is the basic way of grassland utilization, and reasonable grazing is an important way to maintain the health of the grassland ecosystem. However, the traditional grazing time in warming seasons is negative for sustainable desert steppe ecosystem. Determining reasonable grassland grazing methods is to remain a critical issue for the ecological conservation and rational utilization of desert steppe. Therefore, our objectives were to explore the effects of warming seasonal rotation grazing on the species diversity and functional diversity of grassland plants and to reveal controlling factors of plant community diversity. The warm‐season rotational grazing modes included traditional time of grazing (FG), delayed start of grazing (YG), early end of grazing (TG), delayed start early end of grazing (YT), and enclosed steppe (CK). The results showed that the important value of Agropyron mongolicum of the gramineae and Lespedeza potaninii of the leguminosae in YG increased by 12.10%–120.66% and 23.57%–34.25% than other treatments (CK, FG, TG, and YT), respectively. Therefore, the YG treatment has more advantages on the IV of A. mongolicum of the gramineae and L. potaninii of the leguminosae. Warming seasonal rotational grazing (FG, YG, TG, and YT) significantly increased the important value of Leymus secalinus by 51.43%–79.64% compared with CK (p < .05). Compared with CK, FG and YG promoted the growth of gramineae and appropriately reduced the proportion of forbs. There was no significant difference in the Shannon–Wiener index between grazing treatments and CK, while the Shannon–Wiener index in YT increased by 21.43% and 15.71% compared with FG and YG (p < .05). The functional richness index in FG and YG significantly decreased by 19.05%–23.81% compared with CK and TG (p < .05). The results of the redundancy analysis showed that the diversity of plant communities was mainly affected by soil‐available nitrogen. These observations indicated that delayed start of grazing can improve the diversity of plant communities by increasing the important value of dominant plants in the community and promoting the growth of gramineous and leguminous plants, thereby optimizing the composition of community structure. Our findings can provide a theoretical basis for formulating a reasonable and scientific grazing period in desert steppe. Grazing is the basic way of grassland utilization and an important way to maintain the health and production stability of grassland ecosystem. However, the traditional grazing in winter and spring is negative for the sustainable development and management of ecosystem in desert steppe. Determining reasonable grassland grazing methods is to remain a critical issue for the ecological conservation and rational utilization of desert steppe. Therefore, our objectives were to explore the effects of warming seasonal rotation grazing on the species diversity and functional diversity of grassland plants, and revealing controlling factors of plant communities. The results of the redundancy analysis showed that the diversity of plant communities was mainly affected by soil‐available nitrogen. These observations indicated that delayed start grazing can improve vegetation diversity. Our findings can provide a theoretical basis for formulating a reasonable and scientific grazing system in desert steppe.
Juniperus macropoda is the only tree species of a cold desert ecosystem that is experiencing high anthropogenic pressure and has a poor regeneration status due to harsh environmental conditions. Due to the limited distribution of Juniperus macropoda in this region, the species have remained largely unexplored in terms of understanding the distribution pattern along the elevation and soil fertility gradients. Therefore, the current research was carried out along the elevational gradient, starting from the base line at 3000 m above sea level (m asl) asl with an elevational plot distance of 180 m. The study revealed that the average density of J. macropoda declined gradually from the first elevation range, i.e., 3000–3180 m asl onward, and extended up to the elevation range of
3900–4080 m asl. However, the average seedling and sapling densities were highest at mid-elevation and extended up to an elevation range of 4080–4260 m asl. The J. macropoda population formed a reverse J-shaped structure only up to 3540–3720 m asl. The maximum total biomass and carbon density were recorded in the lowest elevational range, and decreased subsequently. The primary soil nutrients under study decreased sharply along the elevational gradient. Seedling, sapling and tree distributions had a significantly positive relationship (p < 0.05) with available N, P, K, SOC, silt and
clay contents and were negatively correlated (p < 0.05) with sand contents. The outcome of the study will form the basis for devising a plan for the management and conservation of J. macropoda forests
