Loss of migratory traditions makes the endangered patagonian Huemul deer a year-round refugee in its summer habitat

The huemul (Hippocamelus bisulcus) is endangered, with 1500 deer split into >100 subpopulations along 2000 km of the Andes. Currently occupied areas are claimed-erroneously-to be critical prime habitats. We analyzed historical spatiotemporal behavior since current patterns represent only a fraction of pre-Columbian ones. Given the limited knowledge, the first group (n = 6) in Argentina was radio-marked to examine spatial behavior. Historically, huemul resided year-round in winter ranges, while some migrated seasonally, some using grasslands >200 km east of their current presence, reaching the Atlantic. Moreover, huemul anatomy is adapted to open unforested habitats, also corroborated by spotless fawns. Extreme naivety towards humans resulted in early extirpation on many winter ranges—preferentially occupied by humans, resulting in refugee huemul on surrounding mountain summer ranges. Radio-marked huemul remained in small ranges with minimal altitudinal movements, as known from other subpopulations. However, these resident areas documented here are typical summer ranges as evidenced by past migrations, and current usage for livestock. The huemul is the only cervid known to use mountain summer ranges year-round in reaction to anthropogenic activities. Losing migratory traditions is a major threat, and may explain their presently prevalent skeletal diseases, reduced longevity, and lacking recolonizations for most remaining huemul subpopulations.Fil: Fluck, Werner Thomas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad de Basilea; Suiza. Administración de Parques Nacionales; ArgentinaFil: Smith Flueck, Jo Anne M.. Universidad Nacional del Comahue; Argentina. Parque Protegido Shoonem; Argentina. Deer Lab; ArgentinaFil: Escobar, Miguel E.. Parque Protegido Shoonem; ArgentinaFil: Zuliani, Melina Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Fundación Bariloche; ArgentinaFil: Fuchs, Beat. Deer Lab; ArgentinaFil: Geist, Valerius. University of Calgary; CanadáFil: Heffelfinger, James R.. Arizona Game and Fish Department; Estados UnidosFil: Black de Decima, Patricia Ann. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo; ArgentinaFil: Gizejewski, Zygmunt. Polish Academy of Sciences; ArgentinaFil: Vidal, Fernando. Univerdidad Santo Tomas; Chile. Centro de Conservacion y Manejo de Vida Silvestre; ChileFil: Barrio, Javier. Centro de Ornitología y Biodiversidad; PerúFil: Molinuevo, María Silvina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Departamento de Ciencias Biológicas; ArgentinaFil: Monjeau, Jorge Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Fundación Bariloche; ArgentinaFil: Hoby, Stefan. Berne Animal Park; SuizaFil: Jiménez, Jaime M.. University of North Texas; Estados Unido

What if we decided to take care of everyone who needed treatment? Workforce planning in Mozambique using simulation of demand for HIV/AIDS care

Background: The growing AIDS epidemic in southern Africa is placing an increased strain on health systems, which are experiencing steadily rising patient loads. Health care systems are tackling the barriers to serving large populations in scaled-up operations. One of the most significant challenges in this effort is securing the health care workforce to deliver care in settings where the manpower is already in short supply. Methods: We have produced a demand-driven staffing model using simple spreadsheet technology, based on treatment protocols for HIV-positive patients that adhere to Mozambican guidelines. The model can be adjusted for the volumes of patients at differing stages of their disease, varying provider productivity, proportion who are pregnant, attrition rates, and other variables. Results: Our model projects the need for health workers using three different kinds of goals: 1) the number of patients to be placed on anti-retroviral therapy (ART), 2) the number of HIV-positive patients to be enrolled for treatment, and 3) the number of patients to be enrolled in a treatment facility per month. Conclusion: We propose three scenarios, depending on numbers of patients enrolled. In the first scenario, we start with 8000 patients on ART and increase that number to 58 000 at the end of three years (those were the goals for the country of Mozambique). This would require thirteen clinicians and just over ten nurses by the end of the first year, and 67 clinicians and 47 nurses at the end of the third year. In a second scenario, we start with 34 000 patients enrolled for care (not all of them on ART), and increase to 94 000 by the end of the third year, requiring a growth in clinician staff from 18 to 28. In a third scenario, we start a new clinic and enrol 200 new patients per month for three years, requiring 1.2 clinicians in year 1 and 2.2 by the end of year 3. Other clinician types in the model include nurses, social workers, pharmacists, phlebotomists, and peer counsellors. This planning tool could lead to more realistic and appropriate estimates of workforce levels required to provide high-quality HIV care in a low-resource settings

Eukaryotic phytoplankton flow cytometric results from salp grazing incubations conducted on R/V Tangaroa cruise TAN1810 during Oct-Nov 2018

Dataset: Eukaryotic Phytoplankton Flow Cytometry from Salp Grazing IncubationsThis dataset includes eukaryotic phytoplankton flow cytometric results from salp grazing incubations in order to quantify food web flows through contrasting salp-dominated and salp-absent water parcels near the Chatham Rise off western New Zealand where salp blooms are a predictable phenomenon. Sampling was conducted on R/V Tangaroa (cruise TAN1810) during October-November 2018. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/846664NSF Division of Ocean Sciences (NSF OCE) OCE-1756465, NSF Division of Ocean Sciences (NSF OCE) OCE-175661