Description:
This study investigates the impacts of Acacia invasion on Bornean heath forest (HF) soils. Soil profiles and water properties at one soil depth (< 1 m) and physicochemical properties with Soil Fertility Index(SFI) and Soil Evaluation Factor (SEF) from the topsoil or three soil depths of Acacia-invaded and non-invaded HF were assessed. Acacia invasion modified soil profiles by producing thicker horizons O and B but waning horizon E. Thick leaf litter layer in invaded HF was associated with a significant decrease in soil bulk density, moisture, organic matter (OM), total organic carbon, water volume, and NH4+ but a significant increase in soil temperature, and soil water pH, NO3
Description:
This study investigates the impacts of Acacia invasion on Bornean heath forest (HF) soils. Soil profiles and water properties at one soil depth (< 1 m) and physicochemical properties with Soil Fertility Index(SFI) and Soil Evaluation Factor (SEF) from the topsoil or three soil depths of Acacia-invaded and non-invaded HF were assessed. Acacia invasion modified soil profiles by producing thicker horizons O and B but waning horizon E. Thick leaf litter layer in invaded HF was associated with a significant decrease in soil bulk density, moisture, organic matter (OM), total organic carbon, water volume, and NH4+ but a significant increase in soil temperature, and soil water pH, NO3
Description:
This study of Northern Territory charophytes documents 22 species in 3 of the genera in tribe Chareae, family Characeae, including 15 previously described species (Chara benthamii, C. erythrogyna, C. globularis, C. karolii, C. lucida, C. porteri, C. protocharoides, C. setosa, C. submollusca, C. wightii, C. zeylanica, Lamprothamnium capitatum, L. compactum, L. stipitatum, Lychnothamnus barbatus) of which 2 are new for the Australian flora (C. erythrogyna and C. wightii), as well as 5 varieties raised to species rank (C. aridicola, C. arnhemensis, C. bancroftii, C. behriana, C. duriuscula), and 2 newly described species (C. lamprothamniformis, C. schultae). Three previously reported species in the tribe (C. braunii, C. corallina, C. fibrosa) are not recorded from the Northern Territory in this study, as previous records were based on erroneous identifications or localities. Although Northern Territory specimens of Lychnothamnus barbatus have not been seen, it has been included in this treatment, because it occurs in south-eastern Queensland, the Gulf of Carpentaria, Papua New Guinea and Timor-Leste. A key, illustrations and descriptions of all the species are provided.
Description:
This study of Northern Territory charophytes documents 22 species in 3 of the genera in tribe Chareae, family Characeae, including 15 previously described species (Chara benthamii, C. erythrogyna, C. globularis, C. karolii, C. lucida, C. porteri, C. protocharoides, C. setosa, C. submollusca, C. wightii, C. zeylanica, Lamprothamnium capitatum, L. compactum, L. stipitatum, Lychnothamnus barbatus) of which 2 are new for the Australian flora (C. erythrogyna and C. wightii), as well as 5 varieties raised to species rank (C. aridicola, C. arnhemensis, C. bancroftii, C. behriana, C. duriuscula), and 2 newly described species (C. lamprothamniformis, C. schultae). Three previously reported species in the tribe (C. braunii, C. corallina, C. fibrosa) are not recorded from the Northern Territory in this study, as previous records were based on erroneous identifications or localities. Although Northern Territory specimens of Lychnothamnus barbatus have not been seen, it has been included in this treatment, because it occurs in south-eastern Queensland, the Gulf of Carpentaria, Papua New Guinea and Timor-Leste. A key, illustrations and descriptions of all the species are provided.
Description:
This study of Northern Territory charophytes deals with the tribe Nitelleae in family Characeae. We recognise 16 species of Nitella for the Territory. The list includes seven previously described species (Nitella belangeri, N. biformis, N. congesta, N. heterophylla, N. micklei, N. myriotricha and N.tumulosa, of which N. belangeri and N. tumulosa are newly recorded for the Australian flora), and nine newly described species (N. acanthospora, N. boreali-australis, N. crocodylus, N. limosa, N.martinii, N. nitida, N. oollooensis, N. silicea and N. townsendii). Of the five previously reported Nitella species in the Northern Territory (N. hyalina, N. myriotricha, N. penicillata, N. pseudoflabellata and N. subtilissima), only N. myriotricha is recorded in this study, because the other records were based on erroneous identifications or localities. All Nitella species described here can be distinguished on the basis of their morphology and reproductive arrangement. Keys, illustrations and descriptions of all the species are provided.
Description:
This study of Northern Territory charophytes deals with the tribe Nitelleae in family Characeae. We recognise 16 species of Nitella for the Territory. The list includes seven previously described species (Nitella belangeri, N. biformis, N. congesta, N. heterophylla, N. micklei, N. myriotricha and N.tumulosa, of which N. belangeri and N. tumulosa are newly recorded for the Australian flora), and nine newly described species (N. acanthospora, N. boreali-australis, N. crocodylus, N. limosa, N.martinii, N. nitida, N. oollooensis, N. silicea and N. townsendii). Of the five previously reported Nitella species in the Northern Territory (N. hyalina, N. myriotricha, N. penicillata, N. pseudoflabellata and N. subtilissima), only N. myriotricha is recorded in this study, because the other records were based on erroneous identifications or localities. All Nitella species described here can be distinguished on the basis of their morphology and reproductive arrangement. Keys, illustrations and descriptions of all the species are provided.
Classifying excessive exercise : examining the relationship between compulsive exercise with obsessive-compulsive disorder symptoms and disordered eating symptoms
Description:
Summary Animal mass mortality events (MMEs) will increase with weather and climate extremes. MMEs can add significant stress to ecosystems through extraordinary nutrient pulses or contribute to potential disease transmission risks. Given their efficient removal of carrion biomass from landscapes, we argue here for the potential of scavenger guilds to be a key nature-based solution to mitigating MME effects. However, we caution that scavenger guilds alone will not be a silver bullet. It is critical for further research to identify how the composition of scavenger guilds and the magnitude of MMEs will determine when scavengers will buffer the impacts of such events on ecosystems and when intervention might be required. Some MMEs are too large for scavengers to remove efficiently, and there is a risk of MMEs subsidizing pest species, altering nutrient cycling or leading to disease spread. Prioritizing native scavenger taxa in conservation management policies may help to boost ecosystem resilience through preserving their key ecological services. This should be part of a multi-pronged approach to MME mitigation that combines scavenger conservation with practices such as carcass dispersal or removal when exceeding a threshold quantity. Policymakers are urged to identify such thresholds and to recognize both the insects and the vertebrate scavengers that could act as allies for mitigating the emerging problem of climate-driven MMEs.
Description:
Coconut water is used as an alternative to conventional sports drinks for hydration during endurance cycling; however, evidence supporting its use is limited. This study determined if drinking coconut water compared to a sports drink altered cycling performance and physiology. In a randomized crossover trial, 19 experienced male (n = 15) and female (n = 4) cyclists (age 30 ± 9 years, body mass 79 ± 11 kg, (Formula presented.) O2 peak 55 ± 8 mL·kg
Description:
Coconut water is used as an alternative to conventional sports drinks for hydration during endurance cycling; however, evidence supporting its use is limited. This study determined if drinking coconut water compared to a sports drink altered cycling performance and physiology. In a randomized crossover trial, 19 experienced male (n = 15) and female (n = 4) cyclists (age 30 ± 9 years, body mass 79 ± 11 kg, (Formula presented.) O2 peak 55 ± 8 mL·kg
Description:
Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
Description:
Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.