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To account for progress towards conservation targets, monitoring systems should capture not only information on biodiversity but also knowledge on the dynamics of ecological processes and the related effects on human well-being. Protected areas represent complex social-ecological systems with strong human-nature interactions. They are able to provide relevant information about how global and local scale drivers (e.g., climate change, land use change) impact biodiversity and ecosystem services. Here we develop a framework that uses an ecosystem-focused approach to support managers in identifying essential variables in an integrated and scalable approach. We advocate that this approach can complement current essential variable developments, by allowing conservation managers to draw on system-level knowledge and theory of biodiversity and ecosystems to identify locally important variables that meet the local or sub-global needs for conservation data. This requires the development of system narratives and causal diagrams that pinpoints the social-ecological variables that represent the state and drivers of the different components, and their relationships. We describe a scalable framework that builds on system based narratives to describe all system components, the models used to represent them and the data needed. Considering the global distribution of protected areas, with an investment in standards, transparency, and on active data mobilisation strategies for essential variables, these have the potential to be the backbone of global biodiversity monitoring, benefiting countries, biodiversity observation networks and the global biodiversity community.

Remote sensing (RS)—taking images or other measurements of Earth from above—provides a unique perspective on what is happening on the Earth and thus plays a special role in biodiversity and conservation applications. The periodic repeat coverage of satellite-based RS is particularly useful for monitoring change and so is essential for understanding trends, and also provides key input into assessments, international agreements, and conservation management. Historically, RS data have often been expensive and hard to use, but changes over the last decade have resulted in massive amounts of global data being available at no cost, as well as significant (if not yet complete) simplification of access and use. This chapter provides a baseline set of information about using RS for conservation applications in three realms: terrestrial, marine, and freshwater. After a brief overview of the mechanics of RS and how it can be applied, terrestrial systems are discussed, focusing first on ecosystems and then moving on to species and genes. Marine systems are discussed next in the context of habitat extent and condition and including key marine-specific challenges. This is followed by discussion of the special considerations of freshwater habitats such as rivers, focusing on freshwater ecosystems, species, and ecosystem services.

Ecological forecasting-making predictions about the ecological consequences of various types of change-is constrained by a variety of factors, including computer model interoperability. This, in turn, is limited by technical barriers, such as semantic and format consistency, and non-technical ones, such as model isolation that results when sponsors use a "stovepiped" funding approach rather than an integrated one. Because these constraints limit the types of ecological questions that can be addressed, they need to be overcome if decision makers are to obtain the information they require to make informed decisions. The problem of limited model interoperability exists, to varying degrees, in all disciplines that rely on computer models, including hydrology, air quality, and the ocean sciences, among others. One solution is being called the Model Web, a concept for an open-ended system of interoperable computer models and databases communicating via Service Oriented Architectures. The Model Web would consist of a distributed, multidisciplinary network of independent, interoperating models (plus related datasets and sensors). Like the World Wide Web it would grow organically, without central control, within a framework of broad goals and data exchange standards or guidelines. These are under discussion but they should emerge naturally from the modeling community; no single

Earth scientists are engaged in integrating knowledge stemming from different disciplines concerned with the constituent parts of the complex Earth system. The goal is to understand Earth's properties as a whole and thereby deliver benefits to society. The scope and complexity of Earth system investigations demand the formation of distributed, multidisciplinary collaborative teams, which presents both scientific and technological challenges. The information systems used by the different disciplines are characterized by heterogeneous and distributed data and metadata models, different semantics and expertise, diverse protocols and interfaces, and different data policies and security levels. We describe how the Global Earth Observation System of Systems (GEOSS) infrastructure facilitates the formation of these distributed, multidisciplinary and collaborative teams. Initiated in 2005 by the GEO Architecture and Data Committee, the GEOSS Interoperability Process Pilot Project (IP3) is a medium for reaching interoperability between disparate components contributed to GEOSS from multi-disciplinary Earth and Space Science communities. In 2007 one of IP3's key successes was an end-to-end demonstration that combined biological species data, accessed through the GBIF (Global Biodiversity Information Facility) contribution to GEOSS, and climate model output data, part of the WMO's Information System's contribution to GEOSS, accessed through NCAR, and seamlessly fed the data stream into an ecological niche model (ENM). The ENM runs in an open modeling framework to produce a new product displaying the impacts of climate change on the geographic distribution of selected

We offered cystic fibrosis (CF) carrier testing to reproductive-age enrollees in an HMO, in order to determine factors associated with test utilization in a primarily nonpregnant population. Male and female enrollees either were mailed an invitation to have the test after attending an educational session (N = 2,713) or were approached in waiting rooms at the HMO sites and given the opportunity to have the test without making an additional visit (N = 608). Uptake was considerably higher when testing could be obtained without making an additional visit (23.5%) than when attendance at an educational session was required as a prerequisite for having the test (3.7%). Utilization was higher among respondents who were planning children. Caucasians, and those with higher educational attainment. Among respondents planning to have children, individuals with higher tolerance for test uncertainty, lower fear of stigma, and higher perceived risk of being a carrier were significantly more likely to have the test. Testing decisions were not associated with the perceived burden of a child with CF or with the likelihood of aborting for CF. Although utilization of CF carrier testing is relatively low among nonpregnant individuals, uptake is significantly higher when testing can be obtained with minimal effort. Factors associated with the decision to be tested had more to do with implications of being a carrier per se than with the concerns of having a child with CF.