Spatial and environmental drivers of macrophyte diversity and community composition in temperate and tropical calcareous rivers

The hypothesis was examined that sources of variation in macrophyte species richness (alpha-diversity: S) and community composition (“species-set”), attributable to spatial and environmental, variables, may differ in importance between tropical and temperate calcareous rivers (>10 mg CaCO3 L−1). To test this hypothesis geographic, environmental, and aquatic vegetation data was acquired for 1151 sites on calcareous rivers within the British Isles, supporting 106 macrophyte species (mean S: 3.1 species per sample), and 203 sites from Zambian calcareous rivers, supporting 255 macrophyte species (mean S: 8.3 species per sample). The data were analysed using an eigenfunction spatial analysis procedure, Moran’s Eigenvector Maps (MEM), to assess spatial variation of species richness and community composition at large regional scale (>105 km2: British Isles and Zambia); and at medium catchment scale (104–105 km2: British Isles only). Variation-partitioning was undertaken using multiple regression for species richness data, and partial redundancy analysis (pRDA) for community data. For the British Isles, spatial and environmental variables both significantly contributed to explaining variation in both species richness and community composition. In addition, a substantial amount of the variation in community composition, for the British Isles as a whole and for some RBUs, was accounted for by spatially-structured environmental variables. In Zambia, species richness was explained only by pure spatial variables, but environmental and spatially-structured environmental variables also explained a significant part of the variation for community composition. At medium-scale, in the British Isles, species richness was explained by spatial variables, and only for four of the six RBUs

an introduction to personalized ehealth

Personalized medicine can be defined as the adaptation of medical treatments to the specific characteristics of patients. This approach allows health providers to develop therapies and interventions by taking into account the heterogeneity of illnesses and external factors such as the environment, patients' needs, and lifestyle. Technology could play an important role to achieve this new approach to medicine. An example of technology's utility regards real-time monitoring of individual well-being (subjective and objective), in order to improve disease management through data-driven personalized treatment recommendations. Another important example is an interface designed based on patient's capabilities and preferences. These could improve patient-doctor communication: on one hand, patients have the possibility to improve health decision-making; on the other hand, health providers could coordinate care services more easily, because of continual access to patient's data. This contribution deepens these technologies and related opportunities for health, as well as recommendation for successful development and implementation

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Sustainability Agenda for the Pantanal Wetland: Perspectives on a Collaborative Interface for Science, Policy, and Decision-Making.

Building bridges between environmental and political agendas is essential nowadays in face of the increasing human pressure on natural environments, including wetlands. Wetlands provide critical ecosystem services for humanity and can generate a considerable direct or indirect income to the local communities. To meet many of the sustainable development goals, we need to move our trajectory from the current environmental destructive development to a wiser wetland use. The current article contain a proposed agenda for the Pantanal aiming the improvement of public policy for conservation in the Pantanal, one of the largest, most diverse, and continuous inland wetland in the world. We suggest and discuss a list of 11 essential interfaces between science, policy, and development in region linked to the proposed agenda. We believe that a functional science network can booster the collaborative capability to generate creative ideas and solutions to address the big challenges faced by the Pantanal wetland

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost