The essentiality of essential biodiversity variables: implementation in nature conservation policies

Biological diversity on Earth is in decline. The current decline in biodiversity has been called (the onset of) the sixth mass extinction. Land cover change causes deforestation and loss of other ecosystems, such as wetlands, resulting in a loss
of crucial habitats for various species. The importance of biodiversity for life on Earth, valued for its own value as well as its relevance to the well-being of humans, has been widely recognized in nature conservation policies and treaties. Policies on (inter)national, regional and local levels set goals and targets that aim to preserve the diversity of ecosystems and species and halt biodiversity loss. To track the progress towards these goals indicators are used. Indicators are variables that signal the state of biodiversity and provide information about whether biodiversity is stable, improving, or declining. Indicators can measure the compositional, structural, and functional biodiversity attributes at various geographical extents. Composition relates to the particular species groups or individual species found in an ecosystem. Structural components of an ecosystem describe the horizontal and vertical vegetation structure, such as vegetation height, leaf area size, and the presence and the amount of deadwood. Functional attributes form the dynamic processes in an ecosystem. A change in compositional, structural, and functional attributes may indicate a change in biodiversity.
To obtain information about the biodiversity in an ecosystem, traditionally field observations are carried out. However, technological development has made other methods to track biodiversity available. These technologies are remote sensing (RS) and the sampling and analysing of environmental DNA (eDNA). Earth observations with satellites have been available for a few decades, but the application of the technology is not yet standardized in biodiversity monitoring. Sampling the environment for DNA of species presence is relatively new. The use of these technologies results in information products that deliver information about ecosystem properties that may have a link with biodiversity. These information products are called ‘RS/eDNA biodiversity products’. The loss of biodiversity is of global concern. In an effort to harmonise the way biodiversity is monitored globally, and to prioritize biodiversity variables that should be monitored to capture possible biodiversity change across different biomes, the scientific networking group GEO-BON (Group on Earth Observations Biodiversity Observation Network) has developed the Essential Biodiversity Variables (EBVs). This framework currently consists of 20 variables that are proposed for harmonised monitoring. In this thesis, the application of RS/eDNA biodiversity products according to policy requirements has been assessed, in light of the EBV framework. The variables in the EBV framework have been used as a method of comparison between the variables that can be monitored with advanced scientific techniques and the indicators of nature conservation policies. Thus, three variable sets (RS/eDNA biodiversity products, indicators used in conservation policies, and the EBV framework) have been compared. For this purpose, literature available about remote sensing and eDNA, the indicators that are monitored by the different nature conservation policies, and the EBVs has been studied. Conservation policies of four different countries (Australia, Germany, Finland, and the Netherlands), as well as national and international policies (UN Convention on Biological Diversity, Sustainable Development Goals, Ramsar Convention on Wetlands, the European Habitat Directive), were part of these studies and have been linked to case studies where RS/eDNA biodiversity products were applied to protected marine and forest areas.
One of the main points that emerges from Chapters 2 and 3 is that the indicators of nature conservation policies emphasize compositional and structural biodiversity attributes. Therefore, traditional biodiversity monitoring related to policy requirements mostly collects information about species composition and ecosystem structure. The species groups that are monitored are usually the species that are characteristic for the environment, such as the number of dominant tree species. This is observed in policies of all countries, as well as international policies. The use of eDNA could provide the opportunity to collect additional information on species groups that are currently often overlooked with traditional monitoring, such as fungi and insects. Studies focusing on the application of the technique in smaller areas have proven its current use and its possibilities. The practical use of eDNA for large-scale monitoring is still in the early stages of development and does not currently seem to be a viable option for reporting on the conservation status related to policy reporting. The implementation of this technique is promising but needs to be developed further for broad application in biodiversity monitoring according to policy requirements. Another important finding was that the monitoring of functional attributes of ecosystems is not the main focus of conservation policies, even though proper ecosystem function is essential for maintaining biodiversity. There are many RS biodiversity products available that can be used to monitor ecosystem function and Chapters 2 and 3 present a mismatch between monitoring requirements and the availability of RS/eDNA biodiversity products. While changes in ecosystem functioning (like changes in ecosystem productivity) can serve as an early warning signal, it is mostly species composition and ecosystem structure that are used as indicators in monitoring programs, resulting in an incomplete biodiversity assessment. Another major aspect of the findings is that the application of remote sensing derived variables usually requires an adaptation of the indicators used in monitoring programs and conservation policies. Often, remote sensing derived variables cannot be used directly for monitoring when an existing monitoring program is already in place. That is mostly caused by two frequent occurring problems that are both about a mismatch between scientific variables and indicators used in monitoring programs. Firstly, when biodiversity information required for reports about the conservation status of a particular area is similar to what remote sensing can deliver, the measurement units in which the variable is expressed are often quite different. For example, a water quality parameter monitored by the Australian New South Wales Estuary Health Assessment program is turbidity, which is measured in nephelometric turbidity unit (NTU, an index). The estimates of turbidity based on algorithms using satellite imagery are often provided in suspended matter (grams per litre) or a different index. This mismatch requires additional research, to understand the correlation between the different variables. The alignment of variables is especially important when monitoring programs are to be connected to various purposes and/or different policies. Also, it should be considered that the variable might be important for local, national, and international policies and thus should be suitable for aggregation. It should be clear how the variable can provide information that is relevant locally and regionally. For example, productivity can be scaled from local to regional and global, as it is expressed in biomass production in a given area and period of time. Another frequently encountered problem is the availability of data. When comparing temporal data collected for an existing monitoring program with Sentinel-2 imagery, it often occurs that the dates on which in situ data is collected, do not corresponds with the dates of available imagery. This can create problems with correlating different datasets. One of my suggestions therefore is, to design a monitoring program that has aligned variables from the start and incorporates fly-over times of satellites. The timing of collecting data about the state of biodiversity could be important for specific biodiversity reporting. The completeness of monitoring programs can be improved when traditional monitoring is accompanied by information derived from RS biodiversity products and interpretation thereof. Improved means here, to ensure that all biodiversity attributes (composition, structure, and function) are monitored, and not just mostly compositional and structural attributes. For improvement, three things are important: 1) clear definition of measured units, 2) alignment of measured units between RS biodiversity products and indicators, and 3) monitoring programs should add more indicators that track functional attributes to the ‘monitoring-mix’. The framework of Essential Biodiversity Variables aim to overcome these problems and provide opportunities for similar assessments of different biomes. However, it is vital that there is an ecological understanding of the monitored areas, to help finetune to a specific environment. EBVs are multi-tiered and consist of different classes, candidates, and measured units. A clear definition of what variables are used within the context of the EBVs is crucial to create assessments with impact and allow for evidence-based decision-making.