THE ECOLOGICAL IMPACTS OF SIGNAL CRAYFISH IN UPLAND STREAM ECOSYSTEMS

Non-native species are an important driver of global biodiversity loss. Worldwide, crayfishes are one of the prominent groups of non-native species. In this study, the American signal crayfish Pacifastacus leniusculus, the most widespread non-native species in Europe, was used as a model invasive crayfish species to determine the impacts and factors driving the dispersal of non-native species in upland stream ecosystems of northeast England. Strong impacts of signal crayfish on stream biota over short (~7 weeks), medium (7 years) and long (28 years) timescales was evident through a combination of controlled mesocosm study, field surveys of a large number of streams and historical data. Density-dependent impacts of crayfish on multiple components of ecosystems including algal growth, leaf litter decomposition, macroinvertebrates and benthic indigenous fish were revealed. Stable isotope analyses showed a significant change in the trophic position of benthic fish in relation to crayfish density but it remained unchanged for crayfish. Decreased abundance of benthic fishes and young-of-year salmonids were recorded over time in crayfish-invaded streams whereas an opposite trend was recorded in uninvaded streams. Benthic fish disappeared in two invaded streams. Three uninvaded streams were invaded between 2011 and 2018. Dramatic declines in macroinvertebrate abundance and taxonomic richness were recorded in invaded streams and stream reaches compared to uninvaded controls. This thesis also identified the factors driving the dispersal of invading crayfish in upland streams through the analysis of crayfish personality, propagule pressure and habitat suitability. Study of three population conditions (fully-established, newly-established and invasion front) revealed that crayfish dispersal in invaded habitats is context dependent. Personality traits played an important role in dispersal, especially at the invasion front but other factors including local population density and availability of refuges also play a key role. Apart from conventional personality traits (e.g. activity, distance moved and exploration), climbing ability, a trait that has received less attention in behavioural studies, was found to influence crayfish dispersal at newly-established and invasion front sites. Currently, no single method is effective in controlling the spread of non-native crayfish to new sites, and at locations where invasive crayfish already exist. Therefore, improvement of existing legislative measures and raising awareness through education are very much needed to reduce intentional and unintentional introductions. In invaded habitats, if early detection is possible, damage can, potentially, be minimised through existing control methods. In-stream barriers may offer promise in controlling crayfish invasion in streams but this requires further research to validate and optimise designs. Findings of this thesis have contributed to our understanding of biological invasion, especially in upland stream ecosystems and underline the importance of managing crayfish invasion

Livelihood of the fishermen in Monirampur Upazila of Jessore district, Bangladesh

The study was conducted to assess the livelihood of fishermen in Monirampur Upazila of Jessore district from July to December, 2012. The mean age and fishing experience of fishermen were 35.22±9.67 and 17.9±7.12 years, respectively. Primary occupation for majority fishermen (90%) was fishing. The mean monthly income of the household (HH) was BDT 9470±4806.89. Only 2% fishermen were landless. 4% fishermen had training on fishing/fish culture. 46% fishermen involved in NGOs for loan and savings. 52% fishermen cultivated paddy during boro (summer) season whereas only 18% cultivated paddy during aman (rainy) season. Major protein sources to the HHs (monthly) were- small indigenous species (SIS) (4.60±2.64 kg), non-SIS (6.31±4.18 kg), meat (3.54±1.67 kg), eggs (18.73±22.20 pieces), and milk (11.10±15.54 liter). The major HH expenditures were- food, education, health, furniture, cloths and others. All fishermen were vulnerable to vabadaha, a situation when water logging takes place during monsoon due to lack of sufficient water drainage system

Fishes of the river Padma, Bangladesh: Current trend and conservation status

The Padma River is one of the longest rivers and it is believed to be an important spawning and feeding ground for riverine fish species of Bangladesh. This study was conducted from February 2013 to January 2014 and with a view to revealing the diversity of fish fauna in the river. A total of 71 species were recorded belonging to 10 orders, 26 families and 54 genera. The most dominant fish order was Cypriniformes contributing 28 species in 16 genera. Cyprinidae was most dominant family contributing 23 species in 16 genera. Four alien species were found. Twenty eight species have been considered threatened by IUCN Bangladesh. These fishes were belonging to the following categories, Vulnerable (13%), Endangered (18%) and Critically Endangered (8%). Comparing the results with the previous findings, it was revealed that the species diversity have declined in the Padma River over time. Considering all the findings it is concluded that the Padma River could be considered a refuge for conservation of threatened freshwater fishes of Bangladesh. The conservation efforts should ensure minimization of anthropogenic impacts, especially the fishing pressure and introduction of alien invasive species

An overview of the traditional rice-prawn-fish farming in Kalia of Narail district, Bangladesh

This study was conducted in Narail district, Bangladesh between January and June, 2012; with a view to describing the status of rice- prawn-finfish based aquaculture practices. Average area of plots was 0.55±0.44 ha, of which mean ditch area was 4.35±2.02% of total land. All farmers dry their plots and made renovation prior to start of a new growing season followed by liming and fertilization. No standard stocking density was maintained, prawns were stocked at 15895 PL/ha, whereas fin fishes at 1551 seeds/ha. Commercial feed was applied in all the plots. Production of prawn, stocked and non-stocked fin fishes were found 380.34±155.25 kg/ha; 713.65±352.99 kg/ha and 51.73±24.55 kg/ha respectively. Average cost and income for fish culture were 120514.07±36758.35 BDT/ha and 232497.48±76594.80 BDT/ha respectively. Average rice production was 4229.78±856.71 kg/ha. Low growth and high mortality of PL; scarcity, high and uprising price of feeds; and floods were identified as the major problems

An overview of the traditional rice-prawn-fish farming in Kalia of Narail district, Bangladesh

This study was conducted in Narail district, Bangladesh between January and June, 2012; with a view to describing the status of rice- prawn-finfish based aquaculture practices. Average area of plots was 0.55±0.44 ha, of which mean ditch area was 4.35±2.02% of total land. All farmers dry their plots and made renovation prior to start of a new growing season followed by liming and fertilization. No standard stocking density was maintained, prawns were stocked at 15895 PL/ha, whereas fin fishes at 1551 seeds/ha. Commercial feed was applied in all the plots. Production of prawn, stocked and non-stocked fin fishes were found 380.34±155.25 kg/ha; 713.65±352.99 kg/ha and 51.73±24.55 kg/ha respectively. Average cost and income for fish culture were 120514.07±36758.35 BDT/ha and 232497.48±76594.80 BDT/ha respectively. Average rice production was 4229.78±856.71 kg/ha. Low growth and high mortality of PL; scarcity, high and uprising price of feeds; and floods were identified as the major problems

Investigation of Phytoplankton and Physico-chemical Parameters in Nursery, Growout and Broodstock Ponds

The study measures the relationship between physicochemical variables with the cell density of phytoplankton in different stages of pond – nursery ponds, grow out ponds and brood stock ponds. The study was conducted on nine fish ponds as three from each category of pond at Natore Government Fish Farm in Bangladesh, during the months of January to June in 2012. The observed physicochemical variables– water temperature, transparency, dissolved oxygen, pH, ammonia-nitrogen, total alkalinity and total hardness – were found within the standard ranges. Four groups of phytoplankton– Bacillariophyceae, Chlorophyceae, Cyanophyceae and Euglenophyceae– werefound among the ponds where Euglenophyceae was recorded highest number almost in all ponds over the study period. Total abundance of different groups of phytoplankton was recorded as mean (±SD) cell density (cell/l) 62.77±2.16×104, 47.22±0.69×104, and 77.12±3.42×104 in nursery pond, grow out pond and brood stock pond, respectively. Overall phytoplankton was found better in brood stock pond than others. Total phytoplankton density has been exhibited significantly positive correlation with DO and inverse relation with water temperature, pH, ammonia-nitrogen and total alkalinity in case of nursery pond. In case of grow out pond, total phytoplankton density has been exhibited significantly positive correlation with temperature and transparency, and significantly negative correlation with others physicochemical characteristics. In case of brood stock pond, total phytoplankton density has no significant relationship with any physicochemical variables of water

Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System

Skin cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. In recent years, deep learning models have shown remarkable success in various computer vision tasks, including image classification. In this research study, we introduce an approach for skin cancer classification using vision transformer, a state-of-the-art deep learning architecture that has demonstrated exceptional performance in diverse image analysis tasks. The study utilizes the HAM10000 dataset; a publicly available dataset comprising 10,015 skin lesion images classified into two categories: benign (6705 images) and malignant (3310 images). This dataset consists of high-resolution images captured using dermatoscopes and carefully annotated by expert dermatologists. Preprocessing techniques, such as normalization and augmentation, are applied to enhance the robustness and generalization of the model. The vision transformer architecture is adapted to the skin cancer classification task. The model leverages the self-attention mechanism to capture intricate spatial dependencies and long-range dependencies within the images, enabling it to effectively learn relevant features for accurate classification. Segment Anything Model (SAM) is employed to segment the cancerous areas from the images; achieving an IOU of 96.01% and Dice coefficient of 98.14% and then various pretrained models are used for classification using vision transformer architecture. Extensive experiments and evaluations are conducted to assess the performance of our approach. The results demonstrate the superiority of the vision transformer model over traditional deep learning architectures in skin cancer classification in general with some exceptions. Upon experimenting on six different models, ViT-Google, ViT-MAE, ViT-ResNet50, ViT-VAN, ViT-BEiT, and ViT-DiT, we found out that the ML approach achieves 96.15% accuracy using Google’s ViT patch-32 model with a low false negative ratio on the test dataset, showcasing its potential as an effective tool for aiding dermatologists in the diagnosis of skin cancer