Wyniki wyszukiwania - Biblioteka Nauki

1

Vascular ground flora in relation to topography, canopy structure and gap light regimes in a subtropical broadleaved forest (South China)

100%

Ou Y. , Su Z. , Ke X. , Li Z.

Polish Journal of Ecology

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2012

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tom Vol. 60, nr 3

463-478

EN

We examined the influence of topography, canopy structure and gap light environmental variables on the patterns of vascular ground flora (vascular plants less than 1 m in height excluding tree seedlings) in a subtropical broadleaved forest in S China, using field data obtained from a 4-ha permanent plot. Both topographic and canopy environmental conditions had a significant effect on community composition, species diversity and distribution of the vascular ground flora. However, topographic factors, especially slope position and aspect, had a greater influence as compared with canopy and understory light conditions. Both number of individuals and number of individuals per species of the ground flora varied significantly with different slope position, aspect, slope steepness and transmitted direct radiation, while species richness varied significantly under different slope position and canopy leaf area index (LAI) The effects of topographic and canopy environmental conditions on ground-flora composition and structure was further confirmed by Canonical Correspondence Analysis (CCA). Multi-response Permutation Procedures (MRPP) showed significant differences in the ground-flora species composition based on all the topographic, canopy structure and gap light variables. Species indicative of topographic, canopy structure and gap light regimes were identified with a significant indicator value (IV - 35%) by Indicator Species Analysis (ISA), which indicated that certain species have their ecological preference for a particular environmental gradient.

2

Seasonal influence of physico-chemical parameters on phytoplankton diversity, community structure and abundance at Parangipettai coastal waters, Bay of Bengal, South East Coast of India

100%

Manigandan V. , Yosuva M. , Saravanakumar A. , Machendiranathan M.

Oceanologia

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2018

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tom No. 60 (2)

114--127

EN

The present investigation studied the seasonal variation between physico-chemical parameters and phytoplankton diversity, community structure and abundance; quantitative samples were collected on a monthly basis from April 2015 to March 2016 at Parangipettai coast, the Bay of Bengal (BOB). Statistical analyses were performed on physico-chemical parameters such as salinity, dissolved oxygen (DO), pH, temperature, nitrate, nitrite, silicate, and inorganic phosphate (IP). The significant (P < 0.0005) variation among seasons as well as a high influence of these parameters was observed on phytoplankton productivity. Totally, 117 species were identified, belonging to five different classes, Coscinodiscophyceae (62%), Bacillariophyceae (17%), Fragilariophyceae (8%), Dinophyceae (8%) and Cyanophyceae (5%). Throughout the study period, the occurrence of most dominant species was observed from class Coscinodiscophyceae and Bacillariophyceae. The phytoplankton species also showed significant changes according to seasonal variations as well as the nutrient availability. Phytoplankton attained their maximum population density during premonsoon; whereas minimum population was observed during monsoon. The performed statistical analysis on phytoplankton species, the Shannon & Wiener diversity index was found to be higher during postmonsoon and lower during monsoon season. The Canonical Correspondence Analysis (CCA) was used, to find out the seasonal relationship between phytoplankton and physicochemical parameters. Hence, the executed CCA results revealed that temperature, salinity, silicate, DO and IP have a higher influence on phytoplankton abundance.

3

Comparison of three modelling approaches for predicting plant species distribution in mountainous scrub vegetation (Semnan Rangelands, Iran)

67%

Chahouki M. A. Z. , Ahvazi L. K. , Azarnivand H.

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tom 60

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nr 2

EN

The predictive modeling of plant species distribution has wide applications in vegetation studies. This study attempts to assess three modeling approaches to predict the plant distribution in the dry (precipitation 128–275 mm) mountainous (altitude 1129–2260 m a.s.l.) scrub vegetation on the example of the rangelands of northeastern Semnan, Iran. The vegetation of the study area belongs to the communities of Artemisia, Astralagus, Eurotia and other scrub species. The main objective of this study is to compare the predictive ability of three habitat models, and to find the most effective environmental factors for predicting the plant species occurrence. The Canonical Correspondence Analysis (CCA), Logistic Regression (LR), and Artificial Neural Network (ANN) models were chosen to model the spatial distribution pattern of vegetation communities. Plant density and cover, soil texture, available moisture, pH, electrical conductivity (EC), organic matter, lime, gravel and gypsum contents and topography (elevation, slope and aspect) are those variables that have been sampled using the randomized systematic method. Within each vegetation type, the samples were collected using 15 quadrates placed at an interval of 50 m along three 750 m transects. As a necessary step, the maps of all factors affecting the predictive capability of the models were generated. The results showed that the predictive models using the LR and ANN methods are more suitable to predict the distribution of individual species. In opposite, the CCA method is more suitable to predict the distribution of the all studied species together. Using the finalized models, maps of individual species (for different species) or for all the species were generated in the GIS environment. To evaluate the predictive ability of the models, the accuracy of the predicted maps was compared against real-world vegetation maps using the Kappa statistic. The Kappa (κ) statistic was also used to evaluate the adequacy of vegetation mapping. The comparison between the vegetation cover of a map generated using the CCA application and its corresponding actual map showed a good agreement (i.e. κ= 0.58). The results also revealed that maps generated using the LR and ANN models for Astragalus spp., Halocnemum strobilaceum, Zygophyllum eurypterum and Seidlitzia rosmarinus species have a high accordance with their corresponding actual maps of the study area. Due to the high level of adaptability of Artemisia sieberi, allowing this specie to grow in most parts of the study area with relatively different habitat conditions, a predictive model for this species could not be fixed. In such cases, a set of predictive models may be used to formulate the environment-vegetation relationship. Finally, the predictive ability of the LR and ANN models for mapping Astragalus spp. was determined as κ = 0.86 and κ = 0.91 respectively, implying a very good agreement between predictions and observations. It is concluded that the combination of mod- elling of the local species distribution constitutes a promising future research area, which has the potentiality to enhance assessments and conservation planning of vegetation (like rangelands) based on predictive species models.

4

Comparison of three modeling approaches for predicting plant species distribution in mountainous scrub vegetation (Semnan Rangelands, Iran)

67%

Chahouki M. , Ahvazi L. , Azarnivand H.

Polish Journal of Ecology

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2012

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tom Vol. 60, nr 2

277-289

EN

The predictive modeling of plant species distribution has wide applications in vegetation studies. This study attempts to assess three modeling approaches to predict the plant distribution in the dry (precipitation 128-275 mm) mountainous (altitude 1129-2260 m a.s.l.) scrub vegetation on the example of the rangelands of northeastern Semnan, Iran. The vegetation of the study area belongs to the communities of Artemisia, Astralagus, Eurotia and other scrub species. The main objective of this study is to compare the predictive ability of three habitat models, and to find the most effective environmental factors for predicting the plant species occurrence. The Canonical Correspondence Analysis (CCA), Logistic Regression (LR), and Artificial Neural Network (ANN) models were chosen to model the spatial distribution pattern of vegetation communities. Plant density and cover, soil texture, available moisture, pH, electrical conductivity (EC), organic matter, lime, gravel and gypsum contents and topography (elevation, slope and aspect) are those variables that have been sampled using the randomized systematic method. Within each vegetation type, the samples were collected using 15 quadrates placed at an interval of 50 m along three 750 m transects. As a necessary step, the maps of all factors affecting the predictive capability of the models were generated. The results showed that the predictive models using the LR and ANN methods are more suitable to predict the distribution of individual species. In opposite, the CCA method is more suitable to predict the distribution of the all studied species together. Using the finalized models, maps of individual species (for different species) or for all the species were generated in the GIS environment. To evaluate the predictive ability of the models, the accuracy of the predicted maps was compared against real-world vegetation maps using the Kappa statistic. The Kappa (K) statistic was also used to evaluate the adequacy of vegetation mapping. The comparison between the vegetation cover of a map generated using the CCA application and its corresponding actual map showed a good agreement (i.e. K= 0.58). The results also revealed that maps generated using the LR and ANN models for Astragalus spp., Halocnemum strobilaceum, Zygophyllum eurypterum and Seidlitzia rosmarinus species have a high accordance with their corresponding actual maps of the study area. Due to the high level of adaptability of Artemisia sieberi, allowing this specie to grow in most parts of the study area with relatively different habitat conditions, a predictive model for this species could not be fixed. In such cases, a set of predictive models may be used to formulate the environment-vegetation relationship. Finally, the predictive ability of the LR and ANN models for mapping Astragalus spp. was determined as K = 0.86 and K = 0.91 respectively, implying a very good agreement between predictions and observations. It is concluded that the combination of modelling of the local species distribution constitutes a promising future research area, which has the potentiality to enhance assessments and conservation planning of vegetation (like rangelands) based on predictive species models.

5

Numerical analysis of bryophyte - environment relationships in a lowland English flora

67%

Bates J. W.

Fragmenta Floristica et Geobotanica

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1995

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tom 40

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nr 1