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Introduction: To address problems of over- and under-treatment with preventive chemotherapy resulting in ongoing transmission of schistosomiasis, the World Health Organization (WHO) recommends targeted mass drug administration (MDA) interventions at a sub-district level. In Tanzania, the lack of sub-district (ward) prevalence data has inhibited a transition to targeted treatment. Model-based prevalence estimation combined with routine surveillance data can be used to overcome this gap. We created a geostatistical model to estimate parasitological prevalence in the wards of the Lake Zone regions of Tanzania to investigate opportunities for enabling targeted MDA for schistosomiasis. With no precedent on how outputs from a geostatistical model could be used to inform decision-making in Tanzania, this qualitative study explores perceptions on what may challenge and motivate program staff in Tanzania's national schistosomiasis control program to integrate the models into routine planning to guide disease control interventions. Methods: Seven semi-structured, key informant interviews were conducted in 2022 examining perceived programmatic challenges and motivations of integrating the geostatistical model into current programming through various thematic areas: information systems, financing, services and operational capacity, policy and planning, and coordination. Key informants included decision-making staff in the Ministry of Health's neglected tropical diseases (NTD) control program, WHO NTD staff, schistosomiasis MDA implementing partners, academic experts studying the control of schistosomiasis, and central-level NTD coordinators. Results: Informants unanimously acknowledged that the geostatistical model could be useful in guiding targeted interventions, and found several factors that may motivate programmatic uptake including providing a financially feasible method to comprehensive prevalence estimates, facilitation of essential implementation tasks like site selection for MDA and screening, as well as annual calculation of treatments required for requesting medicine. Key challenges to integration were seen in limitation of existing modeling expertise, sensitization, and most importantly in the lack of WHO recommendations surrounding model use, as national disease control strategies and policies are built around WHO guidelines. Conclusions: Geostatistical models like the one presented can feasibly be integrated in decision-making for targeted interventions based on domestic capacity, financial availability and readiness. However, the lack of WHO guidance on the use of these tools calls for action to translate the potential of such models into recommendations that encourage routine integration from national programs. Overcoming this key inhibiting factor will be a crucial first step toward the integration of such models. Author summary: Schistosomiasis remains an extremely prevalent disease and a major public health problem in Tanzania despite years of disease control interventions like mass drug administration (MDA). While these interventions have expanded in scale and geographic coverage over the past 15 years, they still fail to reach millions of people who are estimated to need treatment with preventive chemotherapy (PC) drugs, resulting in severe morbidities and death secondary to schistosomiasis infection, and continued disease transmission. In its treatment guidelines, the World Health Organization (WHO) has advocated for national programs to move to a more targeted method of treatment to enable more effective and efficient use of limited resources. Problematically, decision makers in Tanzania don't have disease prevalence data for much of the country that would allow them to plan for such targeted distribution of PC in Tanzania. In 2024, KIT Royal Tropical Institute published a study on how disease prevalence estimates from geostatistical models can be used to fill these inhibitive data gaps and allow country programs to move to targeted treatment as advocated for in the WHO guidelines. With no clear precedent on how use of such models would be integrated into existing schistosomiasis control programs, this study sought to examine perceptions of challenges and incentives to programs associated with integrating these models into the planning process in Tanzania. Through interviews with key informants involved in the planning of schistosomiasis control in Tanzania, the study finds clear actionable barriers to integrating such models in existing systems, despite their perceived utility to support the planning of targeted interventions. In this study, we explore challenges and motivating factors of using this model to better understand blockers inhibiting routine use of such models across highly endemic countries like Tanzania that are stagnating on elimination targets and unable to move to targeted interventions. By examining motivating factors, we further seek to understand what encourages programmatic uptake of models to facilitate their eventual integration into health systems in endemic areas. We believe that results from this study should help to motivate the WHO to standardize recommendations around using geostatistical models, like the one presented in this study, which may serve as powerful tools to better plan interventions and reduce transmission of schistosomiasis. Such recommendations may, as this study found, have a large influence on country uptake of novel tools at country level, helping them to combat a devastating disease that affects so many millions of people across Sub-Saharan Africa and other parts of the world. [ABSTRACT FROM AUTHOR]

Behavioral responses of Risso's dolphins ( Grampus griseus) to whale watching vessels were studied off Pico Island, Azores. Dolphin behavior was studied from a land-based lookout, enabling observations of groups in the absence and presence of vessels. The number of whale watching vessels showed a clear seasonal pattern, dividing the whale watching period into a low season and a high season. During the low season, Risso's dolphins rested mainly in the morning and afternoon. During the high season, Risso's dolphins rested less and did so mainly at noon, when the number of active vessels was lowest. Data analysis using a generalized additive mixed model indicated that this change in resting behavior was associated with vessel abundance. When more than five vessels were present, Risso's dolphins spent significantly less time resting and socializing. During the high season, this vessel abundance was exceeded during 20% of observation days. While we cannot be sure that the observed changes in behavior have fitness consequences for Risso's dolphins, reduced resting and socializing rates can have negative impacts on the build-up of energy reserves and on reproductive success. We suggest the adoption of precautionary management measures to regulate the timing and intensity of whale watching activities. [ABSTRACT FROM AUTHOR]

Large carnivores living in tropical rainforests are under immense pressure from the rapid conversion of their habitat. In response, millions of dollars are spent on conserving these species. However, the cost-effectiveness of such investments is poorly understood and this is largely because the requisite population estimates are difficult to achieve at appropriate spatial scales for these secretive species. Here, we apply a robust detection/non-detection sampling technique to produce the first reliable population metric (occupancy) for a critically endangered large carnivore; the Sumatran tiger (Panthera tigris sumatrae). From 2007-2009, seven landscapes were surveyed through 13,511 km of transects in 394 grid cells (17×17 km). Tiger sign was detected in 206 cells, producing a naive estimate of 0.52. However, after controlling for an unequal detection probability (where p = 0.13±0.017; ±S.E.), the estimated tiger occupancy was 0.72±0.048. Whilst the Sumatra-wide survey results gives cause for optimism, a significant negative correlation between occupancy and recent deforestation was found. For example, the Northern Riau landscape had an average deforestation rate of 9.8%/yr and by far the lowest occupancy (0.33±0.055). Our results highlight the key tiger areas in need of protection and have led to one area (Leuser-Ulu Masen) being upgraded as a 'global priority' for wild tiger conservation. However, Sumatra has one of the highest global deforestation rates and the two largest tiger landscapes identified in this study will become highly fragmented if their respective proposed roads networks are approved. Thus, it is vital that the Indonesian government tackles these threats, e.g. through improved land-use planning, if it is to succeed in meeting its ambitious National Tiger Recovery Plan targets of doubling the number of Sumatran tigers by 2022.