No abstract

No abstract

No abstract

There is substantial environmental variance at small spatial scales (1 m or less) in both natural and disturbed environments. We have investigated the spatial structure of physical variables at larger scales (up to 10 m). We analysed surveys of edaphic properties of Wisconsin forest soils, of the water chemistry of lakes in Ontario and Labrador, and of temperature and precipitation in northeastern North America. We found no clear indication that the variance among sites approaches some maximal value as the distance between them increases. We suggest instead that the variance of the physical environment tends to increase continually with distance. The slope of the log-log regression of variance on distance provides a means of comparing the heterogeneity of different environments with respect to a given factor, or of comparing different factors within a given environment. This slope provides a useful measure of environmental structure that can be related to the biodiversity or plasticity of native organisms.

Human-driven global change is causing ongoing declines in biodiversity worldwide. In order to address these declines, decision-makers need accurate assessments of the status of and pressures on biodiversity. However, these are heavily constrained by incomplete and uneven spatial, temporal and taxonomic coverage. For instance, data from regions such as Europe and North America are currently used overwhelmingly for large-scale biodiversity assessments due to lesser availability of suitable data from other, more biodiversity-rich, regions. These data-poor regions are often those experiencing the strongest threats to biodiversity, however. There is therefore an urgent need to fill the existing gaps in global biodiversity monitoring. Here, we review current knowledge on best practice in capacity building for biodiversity monitoring and provide an overview of existing means to improve biodiversity data collection considering the different types of biodiversity monitoring data. Our review comprises insights from work in Africa, South America, Polar Regions and Europe; in governmentfunded, volunteer and citizen-based monitoring in terrestrial, freshwater and marine ecosystems. The key steps to effectively building capacity in biodiversity monitoring are: identifying monitoring questions and aims; identifying the key components, functions, and processes to monitor; identifying the most suitable monitoring methods for these elements, carrying out monitoring activities; managing the resultant data; and interpreting monitoring data. Additionally, biodiversity monitoring should use multiple approaches including extensive and intensive monitoring through volunteers and professional scientists but also harnessing new technologies. Finally, we call on the scientific community to share biodiversity monitoring data, knowledge and tools to ensure the accessibility, interoperability, and reporting of biodiversity data at a global scale.4

No abstract

The Earthwatch Institute is an international non‐profit organization that works with scientists and scientific institutions to develop citizen‐science‐based research and environmental monitoring programs. Each year, Earthwatch supports close to 80 different projects in more than 30 countries and recruits over 3000 volunteers to aid scientists in collecting data. Participants recruited by Earthwatch seek to tap into their passion for learning about science by volunteering to act as assistants for authentic research projects.

The involvement of non-professionals in scientific research and environmental monitoring, termed Citizen Science (CS), has now become a mainstream approach for collecting data on earth processes, ecosystems and biodiversity. This chapter examines how CS might contribute to ongoing efforts in biodiversity monitoring, enhancing observation and recording of key species and systems in a standardised manner, thereby supporting data relevant to the Essential Biodiversity Variables (EBVs), as well as reaching key constituencies who would benefit Biodiversity Observation Networks (BONs). The design of successful monitoring or observation networks that rely on citizen observers requires a careful balancing of the two primary user groups, namely data users and data contributors (i.e., citizen