Building capacity in biodiversity monitoring at the global scale

Abstract

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

Global Island Monitoring Scheme (GIMS): a proposal for the long-term coordinated survey and monitoring of native island forest biota

“…We then propose and outline a Global Island Monitoring Scheme (GIMS) that is based on permanent forest plots, consistent methods, and selected indicator groups of organisms. Our proposal adds to recent calls for enhanced biodiversity monitoring (Schmeller et al 2017;Stephenson et al 2017;Walters and Scholes 2017) and implies cooperation between environmental agencies responsible for the management of protected areas, and scientists working on the long-term dynamics of biodiversity in native island forests. Our main aim is to propose scientific standards for a global monitoring scheme of spatial and temporal ecological changes in native island forests.…”

Accuracy, limitations and cost efficiency of eDNA‐based community survey in tropical frogs

“…The use of next-generation sequencing approaches led to a boost in data acquisition (Taberlet, Coissac, Pompanon, Brochmann, & Willerslev, 2012) and is considered to make important contributions to biodiversity research (Bohmann et al, 2014;Rees, Maddison, Middleditch, Patmore, & Gough, 2014). eDNA-based metabarcoding may present one of several tools needed to globally coordinate initiatives for ecosystem monitoring and sustainable management (Bush et al, 2017;Schmeller et al, 2017).…”

A general method for estimating seed dormancy and colonisation in annual plants from the observation of existing flora

“…for conservation biology or invasive species management studies) using data collected by citizens (citizen science, Schmeller et al . ) at a large spatial scale. Depending on the data collected, these models could be extended with an HMM approach to take into account abundance data.…”

“…Importantly, as the field data used in the model were simple presence-absence data, this approach could easily be generalised to investigate data in various other types of ecological systems (e.g. for conservation biology or invasive species management studies) using data collected by citizens (citizen science, Schmeller et al 2017) at a large spatial scale. Depending on the data collected, these models could be extended with an HMM approach to take into account abundance data.…”