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    Utilizing supply-demand bundles in Nature-based Recreation offers insights into specific strategies for sustainable tourism management
    (Journal Article, Elsevier BV, 2024) Ghasemi, Mitra; González-García, Alberto; Charrahy, Zabih; Serrao-Neumann, Silvia
    Balancing supply and demand in Nature-based Recreation (NbR) has the potential to yield co-benefits across multiple Ecosystem Services (ES), helping to make tourism activities more sustainable. However, a comprehensive understanding of supply-demand mismatches in NbR is challenging due to the complex interaction among various social, economic and ecological factors. This paper investigates mismatches in NbR supply and demand to provide insights for informing spatial and regional planning to achieve sustainable tourism. To this end, the paper uses a wide range of indicators such as biophysical attributes, accessibility and social indicators to map and assess NbR supply and demand, followed by the application of spatial statistics to analyse supply-demand mismatches. Cluster analysis was performed based on the supply-demand relationship to identify a typology of NbR ES across the study area in the north of Iran. The paper proposes an innovative application of recreation ES bundles with potential implications for sustainable tourism in a region marked as a hot spot for tourism. The analysis generated a typology of five bundles of NbR ES with differing recreational opportunities. Bundles 1 and 2, characterized by a supply surplus and substantial ecological value, are suitable for NbR activities such as camping, hiking, climbing, and birdwatching. In contrast, bundle 4 and 5 associated with urban centres, experience a supply deficit, making them less suitable for NbR. Bundle 3, characterized by a mixture of natural and productive lands, plays an important role in maintaining a balanced supply-demand state. This region holds potential for diverse forms of tourism, including rural and agricultural recreation such as farm tours and farm life experiences. Based on findings, the paper provides valuable insights for spatial and regional planning by proposing targeted strategies to sustainably manage tourism activities.
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    Reduced global processing bias in people with a history of mild traumatic brain injury: Grass everywhere and not a field in sight
    (Thesis, The University of Waikato, 2024) Sidhu, Amanpreet
    Mild traumatic brain injury (mTBI) is increasingly recognized as a disconnection syndrome, due to the prevalence of disrupted long-range connections. Long-range connections refer to neural pathways that span across brain regions, allowing communication and synchronization between different brain areas. Disruption of these connections is associated with the inability to efficiently integrate local information (i.e., multiple visual attributes) into the percept of a single global shape. The aim of this thesis was to investigate whether people with a history of mTBI exhibit a reduced ability to integrate local information into meaningful context. Chapter 2 investigated whether visual integration was impaired in people with a history of mTBI, using visual illusions. Susceptibility to visual illusions demonstrates effective integration of visual information to provide a global picture, with surrounding elements automatically integrated into the perception of local elements. Results from Chapter 2 revealed that people with a history of mTBI exhibited decreased susceptibility to visual illusions, suggesting diminished automatic integration of visual information in a global manner; rather, these individuals appeared to prioritize analysis of local details within a visual stimulus. To further investigate this assumption, we employed the Navon task in Chapter 3 to assess whether people with a history of mTBI demonstrated a tendency to prioritize the processing of local details over global information when presented with multiple objects. This task was chosen as it provides a reliable measure of the extent to which individuals engage in global or local processing. The results revealed that people with a recent history of mTBI (within 12 months) had a reduced bias towards processing global elements of a figure (i.e., attending to the overall configuration of the object), indicating a reduced automatic tendency to process visual information as a coherent global percept. In contrast, individuals who sustained their most recent mTBI more than one year earlier displayed the same global processing bias as control participants (i.e., they attended to the overall structure of the visual stimulus first). Following this observation, it was speculated that people with a history of mTBI may also apply a de-automatized processing style to their movements. It has been argued that individuals with a high propensity to consciously process their movements are more likely to display de-automaticity (inefficient or disrupted movement); therefore, in Chapter 4 we assessed the propensity of people with a history of mTBI to consciously monitor and control their movements, using the Movement Specific Reinvestment Scale (MSRS). The results showed that people with a history of mTBI had a higher propensity for MSRS than controls. In addition, we found that time since most recent mTBI was negatively associated with the conscious motor processing subscale of the MSRS. Chapter 5 builds upon the findings presented in Chapters 2 and 3, delving into the potential implications for real-world behaviours like visual anticipation. Specifically, this chapter investigated the impact of mTBI on individuals' ability to anticipate both deceptive and non-deceptive movements. The results demonstrate that people with a history of mTBI display significantly better anticipation of both deceptive and non-deceptive movements compared to control. Additionally, it was observed that people with a history of mTBI took longer to respond compared to controls. The findings of this thesis suggest that there is an increased tendency to deconstruct visual and movement information following mTBI, highlighting a need for further research to better understand the effects of mTBI on information processing and to develop effective diagnostic and treatment strategies.
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    Climate change increased extreme monsoon rainfall, flooding highly vulnerable communities in Pakistan
    (Journal Article, IOP Publishing, 2023-06-01) Otto, Friederike E. L.; Zachariah, Mariam; Saeed, F; Siddiqi, A; Kamil, S; Mushtaq, H; Arulalan, T; AchutaRao, K; Chaithra, ST; Barnes, C; Philip, S; Kew, S; Vautard, R; Koren, G; Pinto, I; Wolski, P; Vahlberg, M; Singh, R; Arrighi, J; van Aalst, M; Thalheimer, L; Raju, E; Li, S; Yang, W; Harrington, Luke J.; Clarke, B
    As a direct consequence of extreme monsoon rainfall throughout the summer 2022 season Pakistan experienced the worst flooding in its history. We employ a probabilistic event attribution methodology as well as a detailed assessment of the dynamics to understand the role of climate change in this event. Many of the available state-of-the-art climate models struggle to simulate these rainfall characteristics. Those that pass our evaluation test generally show a much smaller change in likelihood and intensity of extreme rainfall than the trend we found in the observations. This discrepancy suggests that long-term variability, or processes that our evaluation may not capture, can play an important role, rendering it infeasible to quantify the overall role of human-induced climate change. However, the majority of models and observations we have analysed show that intense rainfall has become heavier as Pakistan has warmed. Some of these models suggest climate change could have increased the rainfall intensity up to 50%. The devastating impacts were also driven by the proximity of human settlements, infrastructure (homes, buildings, bridges), and agricultural land to flood plains, inadequate infrastructure, limited ex-ante risk reduction capacity, an outdated river management system, underlying vulnerabilities driven by high poverty rates and socioeconomic factors (e.g. gender, age, income, and education), and ongoing political and economic instability. Both current conditions and the potential further increase in extreme peaks in rainfall over Pakistan in light of anthropogenic climate change, highlight the urgent need to reduce vulnerability to extreme weather in Pakistan.
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    'We felt that electricity': Writing-as-becoming in a high school writing class
    (Journal Article, Wiley, 2022-09-13) Rubin, Jessica
    Drawing from data generated in a high school creative writing class, this article presents experiences and moments from a classroom-sited research project that were considered through the theoretical perspective of response-able pedagogies. Using postqualitative methods, this analysis addresses two framing questions: How does turning attention towards the unfolding relations in a writing class illuminate some possibilities of response-able pedagogies? What becomes possible when the teaching of writing emphasises ‘becoming’ (rather than products/achievement)? In response to the first question, turning attention towards the unfolding relations in the class context made new ways of conceptualising writing possible: writing as following energies; writing as making; and writing as producing/traversing boundaries. Considered together, these interwoven practices contributed to the response-able pedagogy of writing-as-becoming. In response to question two, the response-able pedagogy of writing-as-becoming shifted the teaching emphasis from controlled outcomes to the affective experience of connection. This study shows the potential in reconsidering our commitment to teaching writing as (only) a process and to (also) imagine it as a means by which students can experience the vitality and joy of being present with others.
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    Evaluation of New Zealand estuarine water properties using remote sensing
    (Thesis, The University of Waikato, 2024-07) Shao, Zhanchao
    Estuaries and shallow lagoons are the most productive marine systems in the world and contribute to the maintenance of coastal biodiversity by providing various unique habitats for aquatic species. However, due to the inputs of nutrients, sediments and pollutants from the surrounding landscape, light availability and primary productivity can be strongly restricted, which may trigger rapid ecological change over time and cause substantial loss of coastal flora and fauna. This thesis explores the use of Sentinel-2 remote sensing imagery to monitor water properties of shallow intertidal tidal estuaries, for the purposes of managing the estuaries of New Zealand. The first goal (Chapter 2) was to develop a method to remove the influence of water bottom substrate reflectance in order to detect the true water colour (represented by dominant wavelength) and diffuse attenuation coefficients from Sentinel-2 imagery, in the case study estuary of Tauranga Harbour. The new methodology used direct measurement of bottom reflectance of intertidal areas while exposed, and used a regression estimator to derive subtidal bottom reflectance from particle size (developed using the intertidal properties). The method required the water depth to be known (from LiDAR) and reflectance observations from multiple water depths (either extracted along transects or at the same location at different tides). The method was applied to all available Sentinel-2 images and showed seasonal fluctuations and strong correlations with chlorophyll-a, suspended sediments and coloured metal ions collected by the regional council monitoring programme. This methodology was then applied to 12 estuaries and the results were interpreted in terms of ecological changes (Chapter 5). The second goal (Chapter 3) was to detect the distribution or density of seagrass/sandflats (where microphytobenthos (MPB) thrive) and use this as a basis to estimate their gross primary productivity (GPP). The new methodology combined Sentinel-2 imagery, machine learning and literature-derived photosynthesis-irradiance (P – I) curves. The machine-learning model included (1) supervised classification with random forest to delineate seagrass and sandflat areas (2) and three machine learning regressions (artificial neural network (ANN), support vector machine (SVM) and random forest regression (RFR)) to predict the density of seagrass. The result showed ANN was the optimal algorithm to predict seagrass coverage. By adjusting the input water depth and light intensity, the methodology could be further developed to predict the response of seagrass and MPB to sea level rise. To counteract the negative impact of increased turbidity caused by coastal erosion and sediment resuspension due to sea level rise and climate change, controlling sediment loading in coastal waters could be an effective solution for maintaining current productivity throughout the entire harbour. Considering monitoring suspended sediment concentration (SSC) is essential for understanding the resilience of coastal wetlands, a prediction model in Chapter 4 consisting of satellite imagery, numerical simulation and machine learning was developed to enable continuous estimation of SSC. The prediction model included two steps: (1) comparing the Delft3D-derived SSC with corrected satellite data and using K-means classification to categorise the differences into classes; (2) developing a random forest regression model for each class to predict the satellite-derived SSC using Delft3D-derived SSC and other physical parameters. Comparison of the prediction model with in situ measurement showed high accuracy, and the model provided the basis for estimating the accumulated sediment in wetlands. The results showed strong sensitivity to different ways of accounting for incoming SSC supply coastal wetlands. Therefore, employing a model based on real-time observations, like the one developed here, would substantially improve sediment budget estimates in coastal wetlands. Dominant wavelength and diffuse attenuation coefficients (Kd) can form the basis of ecologically relevant indicators. Therefore, in Chapter 5, classification based on these two indicators was developed to cluster New Zealand estuaries with similar states into groups as a basis for management. The dominant wavelength and Kd were derived from the Sentinel-2 images using the seabed correction model developed in Chapter 2. Three groups, including less impacted, moderately impacted and highly impacted, were created which were in broad agreement with other in situ measurements and indicator models such as those focused on light availability and benthic health. Therefore, satellite-derived dominant wavelength and Kd can be two good indicators to reflect estuarine water properties for management.

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