Effective ecosystem monitoring requires a multi-scaled approach

Ecosystem monitoring is fundamental to our understanding of how ecosystem change is impacting our natural resources and is vital for developing evidence-based policy and management. However, the different types of ecosystem monitoring, along with their recommended applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecosystem monitoring, leading to poor program development, implementation and outcomes. In an effort to develop a more consistent and clear understanding of ecosystem monitoring programs, we here review the main types of monitoring and recommend the widespread adoption of three classifications of monitoring, namely, targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables questions relating to where and when ecosystem change is occurring to be addressed. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the causes of ecosystem change. The classification system is flexible and can incorporate different interests, objectives, targets and characteristics as well as different spatial scales and temporal frequencies, while also providing valuable structure and consistency across distinct ecosystem monitoring programs. To support our argument, we examine the ability of each monitoring type to inform on six key types of questions that are routinely posed for ecosystem monitoring programs, such as where and when change is occurring, what is the magnitude of change, and how can the change be managed? As we demonstrate, each type of ecosystem monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, scientists and land managers can design programs best suited to their needs. Finally, we assert that for our most serious environmental challenges, it is essential that we include information from each of these monitoring scales to inform on all facets of ecosystem change, and this is best achieved through close collaboration between the scales. With a renewed understanding of the importance of each monitoring type, along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges

Bijou Hills

53 p. : ill., map ; 24 cm.Includes bibliographical references (p. 51-53)

Predicting risk of hospital and emergency department use for home care elderly persons through a secondary analysis of cross-national data

Abstract Background Older adults remain the highest utilization group with unplanned visits to emergency departments and hospital admissions. Many have considered what leads to this high utilization and the answers provided have depended upon the independent measures available in the datasets used. This project was designed to further understanding of the reasons for older adult ED visits and admissions to acute care hospitals. Methods A secondary analysis of data from a cross-national sample of community residing elderly, 60 years of age or older, and most of whom received services from a local home-care program was conducted. The assessment instrument used in this study is the interRAI HC (home care), designed for use in assessing elderly home care recipients. The model specification stage of the study identified the baseline independent variables that do and do not predict the follow-up measure of hospitalization and ED use. Stepwise logistic regression was used next to identify characteristics that best identified elders who subsequently entered a hospital or visited an ED. The items generated from the final multivariate logistic equation using the interRAI home care measures comprise the interRAI Hospital-ED Risk Index. Results Independent measures in three key domains of clinical complications, disease diagnoses and specialized treatments were related to subsequent hospitalization or ED use. Among the eighteen clinical complication measures with higher, meaningful odds ratios are pneumonia, urinary tract infection, fever, chest pain, diarrhea, unintended weight loss, a variety of skin conditions, and subject self-reported poor health. Disease diagnoses with a meaningful relationship with hospital/ED use include coronary artery disease, congestive heart failure, cancer, emphysema and renal failure. Specialized treatments with the highest odds ratios were blood transfusion, IV infusion, wound treatment, radiation and dialysis. Two measures, Alzheimer’s disease and day care appear to have a protective effect for hospitalization/ED use with lower odds ratios. Conclusions Examination into “preventable” hospitalizations and re-hospitalizations for older adults who have the highest rates of utilization are occurring beneath an umbrella of assuring the highest quality of care and controlling costs. The interRAI Hospitalization-ED Risk Index offers an effective approach to predicting hospitalization utilization among community dwelling older adults.http://deepblue.lib.umich.edu/bitstream/2027.42/109520/1/12913_2014_Article_519.pd

Building a Global Ecosystem Research Infrastructure to Address Global Grand Challenges for Macrosystem Ecology

The development of several large-, "continental"-scale ecosystem research infrastructures over recent decades has provided a unique opportunity in the history of ecological science. The Global Ecosystem Research Infrastructure (GERI) is an integrated network of analogous, but independent, site-based ecosystem research infrastructures (ERI) dedicated to better understand the function and change of indicator ecosystems across global biomes. Bringing together these ERIs, harmonizing their respective data and reducing uncertainties enables broader cross-continental ecological research. It will also enhance the research community capabilities to address current and anticipate future global scale ecological challenges. Moreover, increasing the international capabilities of these ERIs goes beyond their original design intent, and is an unexpected added value of these large national investments. Here, we identify specific global grand challenge areas and research trends to advance the ecological frontiers across continents that can be addressed through the federation of these cross-continental-scale ERIs.Peer reviewe

Integrating data and analysis technologies within leading environmental research infrastructures: Challenges and approaches

When researchers analyze data, it typically requires significant effort in data preparation to make the data analysis ready. This often involves cleaning, pre-processing, harmonizing, or integrating data from one or multiple sources and placing them into a computational environment in a form suitable for analysis. Research infrastructures and their data repositories host data and make them available to researchers, but rarely offer a computational environment for data analysis. Published data are often persistently identified, but such identifiers resolve onto landing pages that must be (manually) navigated to identify how data are accessed. This navigation is typically challenging or impossible for machines. This paper surveys existing approaches for improving environmental data access to facilitate more rapid data analyses in computational environments, and thus contribute to a more seamless integration of data and analysis. By analysing current state-of-the-art approaches and solutions being implemented by world‑leading environmental research infrastructures, we highlight the existing practices to interface data repositories with computational environments and the challenges moving forward. We found that while the level of standardization has improved during recent years, it still is challenging for machines to discover and access data based on persistent identifiers. This is problematic in regard to the emerging requirements for FAIR (Findable, Accessible, Interoperable, and Reusable) data, in general, and problematic for seamless integration of data and analysis, in particular. There are a number of promising approaches that would improve the state-of-the-art. A key approach presented here involves software libraries that streamline reading data and metadata into computational environments. We describe this approach in detail for two research infrastructures. We argue that the development and maintenance of specialized libraries for each RI and a range of programming languages used in data analysis does not scale well. Based on this observation, we propose a set of established standards and web practices that, if implemented by environmental research infrastructures, will enable the development of RI and programming language independent software libraries with much reduced effort required for library implementation and maintenance as well as considerably lower learning requirements on users. To catalyse such advancement, we propose a roadmap and key action points for technology harmonization among RIs that we argue will build the foundation for efficient and effective integration of data and analysis.This work was supported by the European Union’s Horizon 2020 research and innovation program under grant agreements No. 824068 (ENVRI-FAIR project) and No. 831558 (FAIR- sFAIR project). NEON is a project sponsored by the National Science Foundation (NSF) and managed under cooperative support agreement (EF-1029808) to Battell

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes

Physiology and taxonomy of blowflies

Thesis (MAgricSc) -- University of Adelaide, Dept. of Crop Protection, 199