An overview of remote monitoring methods in biodiversity conservation

Abstract

Conservation of biodiversity is critical for the coexistence of humans and the sustenance of other living organisms within the ecosystem. Identification and prioritization of specific regions to be conserved are impossible without proper information about the sites. Advanced monitoring agencies like the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) had accredited that the sum total of species that are now threatened with extinction is higher than ever before in the past and are progressing toward extinct at an alarming rate. Besides this, the conceptualized global responses to these crises are still inadequate and entail drastic changes. Therefore, more sophisticated monitoring and conservation techniques are required which can simultaneously cover a larger surface area within a stipulated time frame and gather a large pool of data. Hence, this study is an overview of remote monitoring methods in biodiversity conservation via a survey of evidence-based reviews and related studies, wherein the description of the application of some technology for biodiversity conservation and monitoring is highlighted. Finally, the paper also describes various transformative smart technologies like artificial intelligence (AI) and/or machine learning algorithms for enhanced working efficiency of currently available techniques that will aid remote monitoring methods in biodiversity conservation.

Keywords: Artificial intelligence; Geographic information system; Molecular techniques; Remote sensing; Wildlife conservation.

Fig. 1

Application of Geographic Information Systems (GIS), remote sensing technologies like radio detection and ranging (RADAR) and satellite-based light detection and ranging (LiDAR) for wildlife monitoring in the forest ecosystem. The figure describes global forest distribution (Our World In Data 2020), wherein displayed GIS and different levels of GIS data, schismatic of passive remote sensing, active remote sensing segregated into primary RADAR system, and a block diagram of satellite-based LiDAR system for generation of DCHM (digital canopy height model) image and 3-D point cloud image of the whole organism is outlined. Global positioning system (GPS), Inertial measurement unit (IMU). The figure is inspired by the following sources: Omasa et al. (2007), Admin (2017), Bhatta and Priya (2017), Jahncke et al. (2018), Martone et al. (2018), Srivastava et al. (2020). The components of the figure are modifications of Portree (2006), Organikos (2012); Smithsonian’s National Zoo and Conservation Biology Institute Smithsonian’s National Zoo (2016), and Freepik (2021). Abbreviations: FP-mode, first-pulse mode; LP-mode, last-pulse mode; DEM, digital elevation model; DTM, digital terrain model

Fig. 2

Molecular techniques for conservation of biodiversity. A Restriction fragment length polymorphism; the genomic DNA extracted from different organisms is PCR amplified and subjected to restriction digestion using specific restriction enzymes, then DNA fragments are separated by electrophoresis and hybridized with radiolabeled probes (Özdil et al. ; Chaudhary and Maurya ; Panigrahi et al. ; Haider and Nabulsi 2020). B Amplified fragment length polymorphism; the restriction fragments of genomic DNA was ligated with compatible adapters and PCR amplified using selective primers against adapters, and the amplified fragments were separated by electrophoresis for DNA fingerprint analysis (Blears et al. ; Malik et al. ; Wu et al. ; Zimmermann et al. ; Neiber et al. 2020). C Random amplified polymorphic DNA; random PCR fragments were amplified from the genome of different species using primers with random sequences and separated by gel electrophoresis to determine the difference between species based on RAPD markers (Panigrahi et al. ; Saikia et al. 2019). D Sequence characterized amplified region; PCR products are generated using primers for RAPD markers from the genome of different varieties and separated in the gel. The polymorphic DNA band is gel extracted and processed for cloning and sequencing for getting specific amplification by designing SCAR primers (Yang et al. ; Bhagyawant ; Ganie et al. ; Cunha and Domingues 2017). E Micro-satellites; the microsatellite repeats were PCR amplified using primers that flank the repeated sequence and separated by gel electrophoresis. The individual bands were cloned and sequenced for analysis of genetic and population diversity (Kim ; Touma et al. 2019). F Expressed sequence tag-simple sequence repeat; cDNA library was prepared from cDNA synthesized from isolated mRNAs, and then end sequencing of cDNA library was performed by EST primers followed by its assembly. The SSR regions were amplified in assembled ESTs using specific primers and the products were analyzed in gel (Rudd ; Sun et al. ; Wagutu et al. 2020). G Inter-simple sequence repeat; the isolated genomic DNA from organisms was subjected to PCR amplification using primers specific to microsatellite, and the PCR products were separated by electrophoresis for analysis (Sarwat ; El Hentati et al. ; Tiwari et al. 2020). H Single nucleotide polymorphism; the genomic DNA from different samples was digested with suitable restriction enzyme followed by adapter ligation and PCR amplification using selective primers. Further to this, PCR products were fragmented with DNase I and labeled with fluorescent probes followed by hybridization in an SNP array. The wells were scanned and data were analyzed (Alsolami et al. ; Scionti et al. ; Cendron et al. ; Kyrkjeeide et al. 2020)

Fig. 3

Application of advanced computer-added physical instruments and associated smart technologies for biodiversity monitoring. The figure describes in a conserved forest ecosystem wildlife and forest ecosystem can be monitored by using autonomous acoustic recording units, animal tracking units (GIS-based), X-ray fluorescence (XRF) analyzer, global positioning system (GPS), and camera trapping units. Configuration of these data from multiple sources and data heterogeneity can be monitored, processed, interpreted, analyzed, and distributed (encrypted) via artificial intelligence and machine-learning-based technologies. Using these technologies wildlife morphology (camera), behavior, phenology, distribution, abundance, phylogeny (acoustic), and diversity with respect to human invasion can be observed. The figure is inspired by the following sources: Bergslien (2013), Buxton et al. (2018), Willi et al. (2018). The components of the figure are modifications of The RAFOS group at the Graduate School of Oceanography, University of Rhode Island, Kingston, RI 02881 (2001), Mudgineer (2011); Leapfrog (2021), Pixabay (2021), Pngtree (2021), and Vecteezy (2021a, b)