approaches and technologies
Recent and ongoing technological developments are reshaping our capacity to monitor biodiversity. Digital connectedness, cataloguing, and storage, combined with advanced analytical techniques and fast computation, provide frameworks that can gather observations across large areas. In Europe, LifeWatch ERIC was established as a European Research Infrastructure Consortium25to provide continued support to the scientific community studying biodiversity. LifeWatch ERIC is building virtual, instead of physical, e-laboratories supplied by the most advanced facilities to capture, standardize, integrate, analyse and model biodiversity (Basset and Los, 2012).
Flexibility and scalability allow designing of monitoring networks for specific tailor-made purposes. Importantly, these frameworks allow expert biologists, citizen scientists, as well as relative non-experts to make valuable contributions (e.g. Chandler et al., 2017). Indeed, these developments facilitate contributions by expert volunteers, who have contributed to biodiversity monitoring for centuries (McKinley et al., 2017). Several tools exist. Functionality varies from geo-locating ones’ own observations to sharing and mapping across the globe. The most well-known citizen-science platform in this context is probably iNaturalist (www.inaturalist.org). iNaturalist provides a generic platform to catalogue species. Another example, which started in 2010, is Pl@ntNet (https://plantnet.org).
Pl@ntNet allows a user to identify a flower, plant or tree species by photographing them with a smartphone (Joly et al., 2014). Specific examples relate to identifying weeds, cultivated and/or ornamental plants, invasive species (Botella et al., 2018) or a focus on specific geographic regions.
Using computer-vision-based algorithms relies on massive amounts of good-quality training data. Pl@ntNet for instance is able to identify a wide range of species with increasing accuracy through a novel collection and validation approach. The Pl@ntNet algorithm continuously learns and improves its accuracy. Each new picture that is submitted provides new data to train the algorithm, benefitting from user feedback stating whether the correct species was identified (or not). These developments highlight that ‘automated plant identification systems are now mature enough for several routine tasks, and can offer very promising tools for autonomous ecological surveillance systems’ (Bonnet et al., 2018). This will drastically reduce the time needed to generate significant biodiversity data flows.
Clearly, there are also limitations. For example, essential taxonomic details may (currently) not be visible on a picture. Resembling a Turing test, Pl@ntNet pitted its algorithms against botanical experts (Bonnet et al., 2018). One of the conclusions was the need for details to make certain taxonomic distinctions.
Ethical considerations also arise. How best to safeguard the geo-location of a protected red-listed species – say an orchid notoriously difficult to find? Surveying is thus undergoing changes. There is still a huge potential in Citizen Science and crowdsourcing. Maintaining such monitoring over longer time periods is one of the challenges. Furthermore, tapping this potential requires having procedures to set standards and for thorough quality. This starts at the collection of observations. The detection and monitoring of pollinator communities, for instance, provides guidance and training to guarantee minimum identification skills of contributing surveyors (LeBuhn et al., 2016). Besides building inventories of species occurrence, near real-time observations underpin better process understanding of why and how species move through landscapes and interact with their environment. Voluntary contributions tracking birds, for instance, have revealed that migratory tracks have been shifting due to climate change (Cooper et al., 2014). New technologies also enable fast and efficient ways to gather data. One example is the efficient collection and extraction of information from pictures with street-level cameras (e.g. d’Andrimont et al., 2018).
Green infrastructures critical for biodiversity can now be mapped at relevant temporal and spatial scales. For example, Lucas et al. (2019) used LiDAR to identify linear vegetation elements in a rural landscape. While new approaches have been developed to retrieve in situ data, the satellite remote sensing community, via the Group on Earth Observations Biodiversity Observation Network (GEO BON, https://geobon.org/ebvs) has developed the concept of Essential Biodiversity Variables (EBVs) (Pereira et al., 2013). They provide the first level of abstraction between low-level primary observations and high-level indicators of biodiversity. So far, there are six EBV classes (genetic composition, species populations, species traits, community composition, ecosystem function and ecosystem structure) with 21 EBV candidates. The EBVs are defined as the derived measurements required to study, report and manage biodiversity change. The EBVs should broker between monitoring initiatives and decision makers.
Some lessons can be learnt from the various experiences of running monitoring schemes and surveys at the supranational level. The first is that setting up a monitoring scheme takes time. Setting up large-scale surveys from planning to execution takes a few years, and this is the case of all surveys described in this chapter. Preparing the sampling scheme, the survey protocol, identifying the funding bodies, running contracts, finding surveyors, actually running the survey and finally preparing the final database and a first analysis of the data easily takes three to five years, and more if, as in the case of birds and butterflies, national schemes have to be set up individually. Therefore, the operational phase should start as soon as the knowledge gap is identified.
Secondly, the role of volunteers is essential. Whether skilled ornithologists and entomologists participating in counts, or people willing to use apps to communicate species and location of plants and animals, biodiversity monitoring cannot occur without volunteers. The cost for monitoring would simply be too high if tens of thousands of volunteers needed to be paid. Moreover, Schmeller et al. (2009) show that volunteer-based schemes can yield unbiased results, if surveys are appropriately designed and the number of volunteers is sufficiently high. In this regard, the raising awareness that biodiversity is under threat will hopefully convey growing interest into active participation in surveys.
The potential of new technologies is high and not yet fully exploited. We can expect in a near future much higher capacity of automatic recognition of plants and animals, improved algorithms for processing images sent by smartphones, higher availability of centralized databases, platforms to store and retrieve information. This will greatly reduce the time needed to be spent on the ground (e.g. taking photos of plants rather than recognizing in the field each single species), but planning, post-processing and maintenance of the infrastructure will still take a considerable share of resources.
Lastly, there is a point that is often undervalued: taxonomy. In order to correctly identify a specimen, a reference must exist, and species must have been named and classified. Moreover experts must be sufficiently trained to correctly classify specimens, and research on this topic should be reported correctly. It is recognized that taxonomy especially for insects and micro-organisms is not complete (FAO,
