Angling to Reach a Destination to Fish—Exploring the Land and Water Travel Dynamics of Recreational Fishers in Port Phillip Bay, Australia

Recreational fishing is a popular pastime and multibillion dollar industry in Australia, playing a key economic role, especially in regional areas. In the State of Victoria, Port Phillip Bay (PPB), bordered by Melbourne and its suburbs, is the largest of the State’s marine recreational fisheries. At present, little is known about the spatial and temporal dimensions of angler travel from origins to destinations, and the applicability of such spatial knowledge in fisheries management. To address this lack of information we assessed spatiotemporal dynamics and patterns in fishing trips, based upon travel distances on land and water, to acquire insight into the spatial ranges over which anglers residing in various locations travel to fishing destinations in the environs of PPB. Data for each angler per fishing trip, from 6,035 boat-based creel surveys, collected at 20 boat ramps in PPB during a 10-year period from 2010 to 2019, were analyzed by applying geospatial modeling. Differences were observed in both land and water travel distance by region and popular target species, with anglers who launched from Bellarine region traveling further on land, and those who targeted snapper traveling further on water. It was also evident that most anglers resided within close proximity of PPB, often less than 50 km, although some anglers traveled long distances across the State to access fishing locations, particularly when targeting snapper. This work further highlights the importance of spatially explicit approaches to inform fisheries management by identifying users across different landscape and seascape scales, and out-of-region or State fishing trips, which may especially impact coastal communities and benefit local businesses.

Geospatial modeling of pre-intervention prevalence of Onchocerca volvulus infection in Ethiopia as an aid to onchocerciasis elimination

Background Onchocerciasis is a neglected tropical and filarial disease transmitted by the bites of blackflies, causing blindness and severe skin lesions. The change in focus for onchocerciasis management from control to elimination requires thorough mapping of pre-control endemicity to identify areas requiring interventions and to monitor progress. Onchocerca volvulus infection prevalence in sub-Saharan Africa is spatially continuous and heterogeneous, and highly endemic areas may contribute to transmission in areas of low endemicity or vice-versa. Ethiopia is one such onchocerciasis-endemic country with heterogeneous O. volvulus infection prevalence, and many districts are still unmapped despite their potential for O. volvulus infection transmission. Methodology/Principle findings A Bayesian geostatistical model was fitted for retrospective pre-intervention nodule prevalence data collected from 916 unique sites and 35,077 people across Ethiopia. We used multiple environmental, socio-demographic, and climate variables to estimate the pre-intervention prevalence of O. volvulus infection across Ethiopia and to explore their relationship with prevalence. Prevalence was high in southern and northwestern Ethiopia and low in Ethiopia's central and eastern parts. Distance to the nearest river (-0.015, 95% BCI: -0.025 - -0.005), precipitation seasonality (-0.017, 95% BCI: -0.032 - -0.001), and flow accumulation (-0.042, 95% BCI: -0.07 - -0.019) were negatively associated with O. volvulus infection prevalence, while soil moisture (0.0216, 95% BCI: 0.014 - 0.03) was positively associated. Conclusions/Significance Infection distribution was correlated with habitat suitability for vector breeding and associated biting behavior. The modeled pre-intervention prevalence can be used as a guide for determining priority for intervention in regions of Ethiopia that are currently unmapped, most of which have comparatively low infection prevalence.

Use of Physical Accessibility Modelling in Diagnostic Network Optimization: A Review

Diagnostic networks are complex systems that include both laboratory-tested and community-based diagnostics, as well as a specimen referral system that links health tiers. Since diagnostics are the first step before accessing appropriate care, diagnostic network optimization (DNO) is crucial to improving the overall healthcare system. The aim of our review was to understand whether the field of DNO, and especially route optimization, has benefited from the recent advances in geospatial modeling, and notably physical accessibility modeling, that have been used in numerous health systems assessment and strengthening studies. All publications published in English between the journal’s inception and 12 August 2021 that dealt with DNO, geographical accessibility and optimization, were systematically searched for in Web of Science and PubMed, this search was complemented by a snowball search. Studies from any country were considered. Seven relevant publications were selected and charted, with a variety of geospatial approaches used for optimization. This paucity of publications calls for exploring the linkage of DNO procedures with realistic accessibility modeling framework. The potential benefits could be notably better-informed travel times of either the specimens or population, better estimates of the demand for diagnostics through realistic population catchments, and innovative ways of considering disease epidemiology to inform DNO.

Geospatial Modeling for Spatial Analysis of Climatic Elements: In search of Climatic Classification Map for Bangladesh

The present study attempts to develop a scientific climatic classification map of Bangladesh using the daily climatic data of rainfall, relative humidity, mean sea level pressure and surface wind speed of the Bangladesh Meteorological Department. There are only two climate classification maps (Koppen-Geiger and Rashid) available for Bangladesh. Rashid relies on a single variable to identically represent a climate zones. In Koppen-Geiger map two weather variables namely Rainfall and Temperature were employed. The Geostatistical tool of ArcMap 10.5 was employed to produce a spatial dataset of the climate classes. In the present climatic classification map, there were three major classes of Dry, Temperate and Humid Temperate and seven sub-classes of Extreme Dry, Dry Low Humid, Temperate with Humidity, Moist Temperate, High Humid and Moisture, Humid Temperate and High Wind Temperate identified. Low annual value of the selected variables found in western and north western part of the country where higher values were found for the south and southeastern part of the country. This research will help to understand the climatic zones and spatial pattern of climatic variables. This will also helpful for future climate, climate risk, hydrological and agricultural research of the country. Keywords: Bangladesh; Climate Variables; Geo-Statistics; Climate Classification; Climate Sub-classes