Introduction

Biological diversity “biodiversity” entails the assortment of earthly life forms heterogeneously ranging from genetic to ecosystem level. It can embrace the evolutionary, ecological, and cultural aspects that uphold life in various forms (McQuatters-Gollop et al. 2019). It fosters ecological functioning that paves the path for fundamental ecosystem services comprising food, water, preservation of soil fertility, and management of pests and diseases (Avigliano et al. 2019; Whitehorn et al. 2019). The plasticity of co-existence between mankind and nature is irreversible because of the symbiotic relationship that sustains the co-survival of humans with other living organisms (Arias-Maldonado 2016).

Biological diversity of forest originating from gene to ecosystem, through species, supports forest habitat that gives rise to fodders and other goods and services in a wide array of diverse biophysical and socio-economic ambience. Despite the applicability and significance of biodiversity, its conservation is vaguely acknowledged. Presently, human invasions have distorted around 75% of the land-based territory and about 66% of the marine ecosystem. Further to this, over a third of the global terrestrial regions are now devoted to domestic pursuit (FAO 2019). Moreover, since 1970, the significance of agricultural crop yield has increased by about 300%, and harvesting of raw timber has hiked by 45%. Moreover, renewable and non-renewable resources roughly of 60 billion tons are presently extracted annually across the globe. Exploitation of land has abridged the prolificacy of 23% of the global land area, annually, up to US$577 billion in worldwide crops are in jeopardy from pollinator loss, and about 100–300 million people are at elevated threat of natural disaster due to loss of coastal habitats and protection (IPBES 2019). If such trends continue then by 2050, the transformative change in nature can lead to an unprecedented devastating irreversible impact on mankind, which will take centuries to recover.

These atrocities of biodiversity need to be averted through proper monitoring and conservation measures. Based on the present advancements in technology, a combination of system-based smart techniques, remote sensing, and molecular approaches will be necessary for implementation of such ambitious conservation drives. Computer-based simulation techniques such as geographic information system (GIS), active and passive radio detection and ranging (RADAR) system, and light detection and ranging (LiDAR) system are playing a crucial role for monitoring biodiversity in real time (Bouvier et al. 2017; Bae et al. 2019; Bakx et al. 2019). Further to this, the application of recent advancements like artificial intelligence (AI) (Kwok 2019) and/or machine learning algorithms (Fernandes et al. 2020) have also been exploited for the same (Hu et al. 2015). These systems are not only reliable in monitoring biodiversity globally but can also help prevent further biodiversity loss worldwide. Besides monitoring tools, conservation of individual species and genetic biodiversity as a whole will require the use of recent molecular techniques. Conservation genomics revolves around the concept that genome-scale data will meliorate the competence of resource proprietors to conserve species. Despite the decades-long utilization of genetic approaches for conservation research, it has only recently been implied for generating genome-wide data which is functional for conservation (Supple and Shapiro 2018). The revolutionary molecular tools like restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP), random amplified polymorphic DNA (RAPD), sequence characterized amplified region (SCAR), microsatellites and mini-satellites, expressed sequence tags (ESTs), inter-simple sequence repeat (ISSR), and single nucleotide polymorphisms (SNPs) have transformed the hierarchy of biodiversity conservation to a higher level (Mosa et al. 2019).

Evidently, more sophisticated monitoring methods such as system-based simulation techniques, remote sensing, artificial intelligence, and geographic information system as well as molecular-based techniques facilitate the monitoring methods in biodiversity conservation and restoration. Therefore, the present 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. 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.

Methodology of literature search

The relevant literature search was done electronically by using Google Search Engine, PubMed, ScienceDirect, SpringerLink, Frontiers Media, and MDPI databases. The most importantly searched keywords were biodiversity and potential threats, techniques to monitoring biodiversity, geographic information system, remote sensing, active remote sensing system, radio detection and ranging (RADAR) system, light detection and ranging (LiDAR) systems, passive remote sensing systems, techniques for identification and genetic conservation of species, restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP), random amplified polymorphic DNA (RAPD), sequence characterized amplified region (SCAR), mini- and micro-satellites, expressed sequence tags (ESTs), inter-simple sequence repeat (ISSR), single nucleotide polymorphisms (SNPs), and artificial intelligence in biodiversity monitoring which were used and placed repeatedly within the text.

Biodiversity and potential threats

Biodiversity in simple terms is a heterogenic distribution of flora and fauna throughout the world or in a particular niche (Naeem et al. 2016). The number of species described around the world as per IUCN (2020) accounts for 2,137,939, of which 72,327 are vertebrates, 1,501,581 are invertebrates, 422,756 are plants, and 141,275 are identified as fungi and protists. It is now acknowledged that biodiversity is a major indicator of community ecosystem fluctuations and functioning (Tilman et al. 2014). These include provisioning of food, pollination, cultural recreation, and supporting nutrient cycling (Harrison et al. 2014; Bartkowski et al. 2015). Biodiversity as a whole is represented by two major components that are species richness and species evenness. A biogeographic region with a significant level of endemic species and with a higher loss of habitat is generally depicted as a biodiversity hotspot (Marchese 2015). These areas have proven themselves as a tool for establishing conservation priorities and orchestrate vital rationale in decision-making for cost-effective tactics to safeguard biodiversity in its natural conserved state. Usually, the hotspots are marked by single or multiple species-based metrics or concentrate on phylogenetic and functional diversity to shield species that sustain exclusive and inimitable functions inside the ecosystem (Marchese 2015).

Currently, as per the IUCN Red List of Species 2020–2021, of the 2,137,939 species around the world, about 31,030 species are categorized as “threatened” species. Among these, plants with 16,460 numbers contribute the most followed by vertebrates (9063), invertebrates (5333), and fungi and protists (174) [IUCN 2020]. Many of the species are still not assessed due to a lack of reliable identification tools or techniques. Biodiversity is mostly threatened by over-population, habitat and landscape modification, indiscriminate exploitation of resources, pollution, and lack of proper documentation (Marchese 2015; Liu et al. 2020; Reid et al. 2019). Demographic changes can be considered an imperative module for assisting the indirect drivers of biodiversity alternations specifically associated with land use patterns (Newbold et al. 2015). Population explosion, central demographic developments, and urbanization impact both ecosystems and the species it harbors (Mehring et al. 2020). As the changing demographic pattern is associated with population explosion, this may pose a pessimistic impact on food availability, restricted emission of greenhouse gases, control of invasive species and diseases, etc. (Lampert 2019; Manisalidis et al. 2020; Hoban et al. 2020; Reid et al. 2019). To generate such massive data over a stipulated time frame and process them simultaneously to extrude applicable information requires cutting-edge tools and multidisciplinary scientific input (Randin et al. 2020). With recent advancements in mapping software, large-scale data processors, and monitoring tools and genetics, artificial intelligence for generating accurate data over a larger area as a part of a global monitoring strategy has now become feasible (Wetzel et al. 2015; Randin et al. 2020). Hence, an assortment of the above mention techniques and tools will be essential for the conservation and restoration of biodiversity.

Techniques for monitoring biodiversity

Mapping and monitoring techniques have been frontiers in predicting and modeling anthropogenic activities, habitat use, and pattern of land use over time in a particular region. These advanced physical techniques include GIS, LiDAR, and RADAR systems (Bouvier et al. 2017; Bae et al. 2019; Bakx et al. 2019).

Geographic information system (GIS)

Understanding functional geography and making intelligent decisions is widely beneficial for naturalists. GIS is a popular tool for analyzing possible and current spatial-temporal distribution, location, distribution patterns, population assessment, and identification of priority areas for their conservation and management (Krigas et al. 2012; Salehi and Ahmadian 2017). Currently, development of ecological niche models based on topographic, bioclimatic, soil, and land use variables was mapped and predicted for species such as Clinopodium nepeta, Thymbra capitata, Melissa officinalis, Micromeria juliana, Origanum dictamnus, O. vulgare, O. onites, Salvia fruticosa, S. pomifera, and Satureja thymbra (Bariotakis et al. 2019). With the assistance of digitally integrated video and audio-GIS (DIVA-GIS), actual geographic distribution and the future potential assortments of several Zingiber sp. like Z. mioga, Z. officinale, Z. striolatum, and Z. cochleariforme were analyzed (Huang et al. 2019). Most recently, important climatic inconsistencies distressing the geographical dispersion of wild Akebia trifoliate based on the formation of spatial database were successfully determined with the help of GIS (Wang et al. 2020). However, GIS possesses certain limitations such as expensive software, hardware, capturing GIS data, and difficulty in their use (Bearman et al. 2016) (Fig. 1, Table 1).

Fig. 1
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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

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Table 1 Application of geographic information system (GIS) in biodiversity monitoring
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Remote sensing

The ability to extract information about the environment without physical contact from a large distance by a sensor that reflects and/ or emits electromagnetic spectrum (visible, infrared, and microwave spectra) is defined as remote sensing. Based on the source of radiation emitted, which comes in contact with the object, remote sensing can be categorized as active or passive remote sensing systems (Höppler et al. 2020). Remote sensing of biodiversity can be used for habitat mapping including species area curve and habitat heterogeneity, species mapping/distribution, plant functional diversity/ traits, spectral diversity including vegetation indices and spectral species (Cavender-Bares et al. 2020; Wang and Gamon 2019).

Active remote sensing system

An active remote sensor emits energy pulses and records the return time and amplitude of the backscattered energy pulses from the object to generate the required information about it (Vogeler and Cohen 2016). Currently used active remote sensing technologies like RADAR and LiDAR systems can be used to determine the location, speed, and direction of any wildlife form.

Radio detection and ranging (RADAR) system

As a sub-set of the active remote sensing system, RADAR, operates in the microwave of wavelengths of 1 mm to 1 m. Additionally, the modern RADAR systems are incorporated with software routines to mathematically enhance spatial resolution and manage multiple pictures of the same object, also as Synthetic Aperture RADARs (SARs) (Fig. 1). These systems can be used to determine the polarization of the emitted and receive electromagnetic rays which provides a better understanding of the analyzed surface properties (Hay 2000; Valbuena et al. 2020; Barlow and O’Neill 2020) (Table 2).

Table 2 Biodiversity monitoring through remote sensing
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There are two basic types of RADAR systems, namely, primary and secondary. In the primary system, the signal is transmitted in all directions however some of the signals are reflected back to the receiver after colliding with the target thereby defining or detecting the location of the target (Hirst 2008; Bhatta and Priya 2017). In this system, the transmitted signal needs to be of high power to ensure that the reflected signal is sufficient enough to provide accurate and precise information about the target (Bhatta and Priya 2017). Again, noise and signal attenuation due to some factors might disrupt the reflected signal which can also be regarded as a limitation (Bhatta and Priya 2017; Martone et al. 2018). In the secondary RADAR system, an active answering signal system has been installed for accuracy, where the transmitted signal is received by a compatible transponder that retrieves the signal and further sends a signal comprising the useful information in a coded form (Hirst 2008; Bhatta and Priya 2017). The receiver receives the coded signal, and after decryption of the code, the information about the target is transcribed, thereby providing information about the real-time spatial orientation (Bhatta and Priya 2017; Jahncke et al. 2018).

Further to this, ultra wideband (UWB) RADAR is one of the traditional methods used for life detection that analyzes the reflected/echo signal received after hitting the target. Micro-motions by humans, nearby environment, and clutter signals can modulate the reflected signal. As per evidence, it is a reasonable, effective, and complete non-invasive life detection method (Chunming and Guoliang 2012; Karthikeyan and Preethi 2018; Yin and Zhou 2019). With the growth of scientific innovation in the field of remote sensing, a hybrid (On-Chip Split-Ring-Based Sensor) RADAR system has emerged with high-resolution range and sensitivity. This system can easily detect multiple life forms simultaneously even across obstacles (Liu et al. 2016).

One of the major applications of RADAR is range detection and to date, the replacement of the sensing and detection efficiency and accuracy by RADAR has not been possible by any other electronic system (Bhatta and Priya 2017; Parrens et al. 2019). Extension of the sensing capability with respect to atmospheric conditions such as rain, snow, smoke, darkness, and fog and collecting the data makes it unexceptional and advantageous. At present, RADARs have broad areas of applications in defense and control systems, monitoring and forecast systems, astronomy, target-locating system and remote sensing, etc. (Bhatta and Priya 2017).

Light detection and ranging (LiDAR) systems

LiDAR is a widely recognized technology, especially the airborne laser scanner (ALS), which focuses on the emission and receipt of laser pulses. During field surveys, LiDAR technology offers the potential to establish variables, representing forest structures that are distinct from those detected or assessed. Bitemporal airborne LiDAR with field survey is widely used for systematic assessment of uncertainties in satellite imagery-based vegetation (Ma et al. 2018). Ground-based field survey with airborne LiDAR is applicable for daily tracking of bats on foot-to-roost trees using various radio receivers and antennas and the location of tree cavities using directional antennas and binoculars from the ground (Carr et al. 2018; Stephenson 2020) (Fig. 1, Table 2).

Unmanned aerial vehicle (UAV) camera with LiDAR data is used for surveying mangrove-inundation spatial patterns in a subtropical intertidal wetland in southeast China (Zhu et al. 2019). With the support of artificial intelligence (AI), LiDAR remote sensing is found to be successful in predicting models for efficient biodiversity study by covering more areas with a clear database in a very span of time. LiDAR plot extracted information and Landsat pixel-based composites along with time-series were effective in modeling sets of reflectance images of forest structure across Canada’s forest-dominated ecosystem (Matasci et al. 2018). More advanced LiDAR technologies have now overtaken the previous ones in terms of both efficacy and accuracy. Almeida et al. (2019) in their research on three seasonal semi-deciduous natural forest cover types in the Atlantic forest biome of Southern Brazil have considered ALS with portable ground LiDAR remote sensing as a proxy for analysis of structural hallmarks of forest canopies enduring restoration. Airborne LiDAR with principal component analysis (PCA) assessed the estimation of canopy structure and biomass of Moso bamboo (Phyllostachys pubescens) in widely distributed subtropical forests of south China (Cao et al. 2019). High-resolution vertical Scheimpflug LiDAR has proven to resolve hypothesis for insects flying over Ostra Herrestad wind farm near the town Shimrishamn in southern Sweden (Jansson et al. 2020).

To conserve plant diversity information on various forest attributes, aboveground biomass (AGB), canopy structure, canopy cover, and leaf area index (LAI) are considered important and can be assessed most efficiently with a technique like LiDAR (Bolton et al. 2020). Forest canopy height could be an important indicator of biodiversity, productivity, and carbon storage (Li et al. 2020b). LiDAR when combined with other RS techniques, i.e., high spatial resolution with hyperspectral sensors, thermal remote sensing, and satellite RS are yielding eye-catching results, particularly in biodiversity and ecosystem conservation.

Passive remote sensing systems

Passive remote sensing is often understood as a system that operates by passive sensors which can only be used for detection in presence of the natural source of energy, i.e., sunlight (visible to shortwave spectrum and infrared thermal radiation) (Srivastava et al. 2020). These sensors have a specialty to detect natural energy (radiation) that is either emitted or reflected from the source of energy or object (Earthdata 2021). Limitations of the applicability of passive remote sensing for biodiversity and ecosystem conservation are its dependency on sunlight as a source of radiation which is again dependent completely on the season, region, and climatic conditions (Fig. 1).

Techniques for identification and genetic conservation of species

Restriction fragment length polymorphism (RFLP)

RFLP is a biallelic, polymorphic genetic marker characterized by hybrid labeled probes of DNA fragments and digested with restriction endonucleases for estimations of genetic diversity (Vignal et al. 2002; Amom and Nongdam 2017). Different restriction sites in DNA represent the genetic divergence between different populations or related species within a population. Özdil et al. (2018) demonstrated genetic diversity among 11 donkey species in Turkey by conducting PCR-RFLP of two genes. The restriction sites of DraII, MboI, and EagI on the lactoferrin gene (LTF) and PstI on the κ-casein gene (CSN3) have been validated to identify the polymorphism among the donkey population (Özdil et al. 2018). Meikasari et al. (2019) have also made elucidation of low genetic diversity among the seahorse (Hippocampus comes) found in Bintan waters. Another recent study indicates the utilization of PCR-RFLP in genetic-based sex determination of Sebastes rockfish (Vaux et al. 2020). The PCR-RFLP application in identification of durum (Triticum durum L.) and bread wheat (T. aestivum) species has also been studied by analyzing chloroplast DNA (Haider and Nabulsi 2020) (Table 3, Fig. 2A).

Table 3 Molecular and Genomic techniques used for conservation of biodiversity
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Fig. 2
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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. 2018; Chaudhary and Maurya 2019; Panigrahi et al. 2019; 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. 1998; Malik et al. 2018; Wu et al. 2019b; Zimmermann et al. 2019; 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. 2019; 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. 2014; Bhagyawant 2015; Ganie et al. 2015; 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 2019; 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 2003; Sun et al. 2019; 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 2012; El Hentati et al. 2019; 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. 2013; Scionti et al. 2018; Cendron et al. 2020; Kyrkjeeide et al. 2020)

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Amplified fragment length polymorphism (AFLP)

AFLP is considered an effective means of detecting polymorphism in DNA without having any prior information regarding the genome. Being a dominant marker, it can analyze multiple loci through amplification of DNA performing PCR reaction (Bryan et al. 2017). The method employs restriction digestion of DNA and amplification of fragments through ligation of adapters on both ends and using primers specific to adapters (Malik et al. 2018). Genetic differences can be identified from the disparity in the number and length of bands on electrophoretic separation. Its application ranges from the assessment of genetic diversity within species to generate of genetic maps for disease diagnosis and phylogenetic studies. AFLP data analysis study represents the genetic diversity in E. tangutorum population contributed by geographical and environmental factors (Wu et al. 2019b). The phylogenetic relationships and genetic distances among A. platensis populations and other distinct related species such as A. georginae and A. ludwigi in southern Brazil were also investigated through AFLP (Zimmermann et al. 2019). Population structure and differentiation among Melanopsis etrusca were clearly distinct between the eastern, western, and central regions populations in Italy (Neiber et al. 2020) (Table 3, Fig. 2B).

Random amplified polymorphic DNA (RAPD)

The RAPD is a PCR-based technique in which 8–10 short nucleotides comprise both forward and reverse primers that bind arbitrary nucleotide sequences of chromosomal DNA to generate random fragments. Due to this random nature of primers, no prior knowledge about genome sequence is needed. The annealing sites of these random primers vary for different species or individual to individual. Discrimination can be identified or determined from the amplified DNA fragments (RAPD markers) separated by agarose gel electrophoresis (Freigoun et al. 2020). RAPD markers are dominant and involved in various applications such as genome mapping, molecular evolutionary genetics, genetic diversity analysis, and population genetics as well as determining taxonomic identity (Qamer et al. 2021). Saikia et al. (2019) deduced genetic variation among the different morphs of muga silkworm of Northeast India through RAPD analysis. Moreover, Sulistyahadi et al. (2020) studied the locus diversity as well as genetic polymorphism of the endemic species Rhacophorus margaritifer population by this technique. It has also been used to elucidate the genetic variation in a medicinal plant species found in the south of Jordan named Artemisia judaica (Al-Rawashdeh 2011) (Table 3, Fig. 2C).

Sequence characterized amplified region (SCAR)

SCAR markers are DNA fragments generated by PCR amplification using specific 15–30-bp long primers derived from RAPD markers through cloning and sequencing (Bhagyawant 2015). Usually, RAPD markers are associated with low reproducibility and are dominant in nature, making it inappropriate for species identification (Sairkar et al. 2016). To overcome this disadvantage, RAPD markers are converted to SCAR markers which are locus-specific and co-dominant in nature (Bhagyawant 2015; Feng et al. 2018). Due to the specificity of primers, PCR amplification of SCARs is less sensitive to reaction condition and thus are easy to perform (Yuskianti and Shiraishi 2010). SCAR markers provide authenticate information both for species identification and population genetic diversity analysis. Researchers have successfully developed SCAR markers for the medicinal plant V. serpens using 1135-bp long amplicon through RAPD obtained by six accessions of the plant, thereby preventing it from extinction (Jha et al. 2020) (Table 3, Fig. 2D).

Mini- and micro-satellites

Mini-satellites (variable number of tandem repeats (VNTRs) 6–100 bp) and micro-satellites (1–6 bp) (simple sequence repeats (SSR) and short tandem repeats (STR)) are randomly repetitive DNA sequences widely dispersed in all eukaryotic species genomes. These multi-allelic markers are co-dominantly inherited with species-specific location and size within the genome (Vergnaud and Denoeud 2000; Vieira et al. 2016). Due to the high level of polymorphism associated with mini and microsatellites, it is extensively utilized in genetic analysis and population studies. Microsatellites are interspersed all over the genome and therefore represent high variability and their identification show great variation among species of the different population (Abdul-Muneer 2014). Its analysis includes PCR amplification of loci by using primers that flank the repeated sequence. By using microsatellite markers, genetic structure of Agu pigs has been elucidated along with its correlation with Ryukyu wild boar, two Chinese breeds and five European breeds (Touma et al. 2019). Similarly, De Góes Maciel et al. (2019) analyzed 13 microsatellite loci of 361 white-lipped peccaries for assessment of their population structure and level of genetic diversity (Table 3, Fig. 2E).

Expressed sequence tags (ESTs)

ESTs are small sequences of DNA usually 200 to 500 nucleotides long that act as tags for the expressed genes in certain cells, tissues, or organs. ESTs are generated by sequencing either the 3′ end or 5′ end of a segment derived from random clones from the cDNA library and long enough for the identity illustration of the expressed gene (Behera et al. 2013). ESTs are widely involved in gene discovery, determining the phylogenetic relationship between individuals, genetic diversity, and proteomic analysis as well as transcriptome profiling (Cai et al. 2015). EST-derived SSR markers are more informative than genomic SSRs for genetic diversity analysis due to several advantages such as high conserved nature, variation in coding sequence, and high heritability to closely related species (Parthiban et al. 2018). Sun et al. (2019) have conducted the structure analyses of expressed sequence tag-simple sequence repeat (EST-SSR) markers in Juglans sigillata and demonstrated the genetic structure based on its geographic feature. Moreover, EST-SSR analyses have provided information regarding the genetic distance between the J. regia and J. sigillata populations. By considering EST-SSRs and genotype sequencing data, they have interpreted iron walnut as the subspecies of J. regia (Sun et al. 2019). Investigation of evolutionary relations and genotypic relatedness are essential for the conservation of endangered species. Recently the genetic variability of an endangered species Magnolia patungensis was studied by analyzing the EST-SSR polymorphic markers (Wagutu et al. 2020) (Table 3, Fig. 2F).

Inter-simple sequence repeat (ISSR)

ISSR markers are used in diversified analyses such as species identification, evolutionary and taxonomic studies, genome mapping, genetic diversity, and gene tagging because of their high polymorphic nature (Arif et al. 2011; Abdelaziz et al. 2020). These multilocus markers are generated through PCR amplification by using microsatellites as primers. Prior sequence knowledge is not required for primer designing as repeat sequence is used to amplify these inter-microsatellite regions (Ng and Tan 2015). It overcomes all the limitations possessed by other markers such as RAPD and AFLP which are associated with low reproducibility (Najafzadeh et al. 2014). Genetic diversity and population structure analysis have been performed among 11 populations of Bergenia ciliata using 15 ISSR markers. The analysis shows a high level of polymorphism among this medicinal plant species, found in the Indian Himalayan Region (Tiwari et al. 2020). El Hentati et al. (2019) have studied genetic diversity and phylogenetic relationships among 20 samples of three geographical local cattle populations using ISSR primers. They found a significant variation and geographical separation among the cattle from the north, northeast, and northwest of Tunisia (Table 3, Fig. 2G).

Single nucleotide polymorphisms (SNPs)

Single nucleotide variation in genetic sequences defines the Single nucleotide polymorphism (SNP) among individuals, generated due to point mutation or replication errors, giving rise to different alleles within a locus (Van den Broeck et al. 2014). SNPs are the most common form of variation present extensively in the non-coding, coding, and inter-genic regions of DNA (Vallejos-Vidal et al. 2019). SNPs are mainly exploited for population structure, genetic diversity, genetic map construction, and identification of particular traits, etc. (Xia et al. 2019). Their abundance in coding regions makes them more attractive markers for the detection of mutations associated with diseases. SNP markers are however less polymorphic than SSR markers due to their biallelic or triallelic nature (Casci 2010; Mammadov et al. 2012). Cendron et al. (2020) demonstrated the population structure and genetic diversity of local Italian chicken breeds by using SNPs for conservation purposes which revealed lower genetic diversity among the local breeds. In another study, genetic diversity and differentiation among the D. ruyschiana populations of the Norwegian region were investigated by analyzing 96 SNPs derived from 43 sites that reported the existence of four distinct genetic groups within the population (Kyrkjeeide et al. 2020) (Table 3, Fig. 2H).

Artificial intelligence in biodiversity monitoring

With the growing performance of computing power and DL in recent years, machines had become significantly more intelligent and reliable than ever. Modern machines can handle more extensive data and more complex DL models than before (Dean 2019; Chen et al. 2020a). Through this progress, machines had achieved the ability to replicate human expertise (Liu et al. 2019b). Currently, several problems exist within our diverse planet. Researchers began to accelerate the development of several AI solutions with DL to preserve the earth for the later generations to come. In most studies, DL method’s employment provided an automated capability for machines to recognize, classify, and detect images, sounds, and behavior of animals, plants, and even humans (Abeßer 2020). According to Klein et al. (2015), one of the primary methods of preserving our biodiversity consists of monitoring and manual data collection. However, frequent conduct of such practices can become tedious and cause disturbances to sensitive wildlife habitats. With that said, monitoring became less reliable and brief (Table 4). AI-based methods have shown that even at its pre-mature level, biodiversity can have an improvement by reducing animal extinction, prolonged and in-depth monitoring of various life forms, unlocking and accessing unexplored areas, and faster and easier classification of species. With the continuing efforts in data duration, transparency, and research collaborations, these technology types may reach far beyond our expectations. These solutions, if appropriately handled, can yield a massive impact to preserve the planet and its resources without involving humans. Furthermore, the implementation of AI-based methods also extends humans’ capability to explore locations that our biological composition cannot handle, leading to discoveries of new species and life. Due to the accessibility of various capture devices, a wide range of collected data through images, videos, audio, and other forms of data fast-tracked DL and AI development. The problematic method and reliance on organic experts to perform a small to large-scale monitoring of animals, plants, and insects became less challenging as automation systems have improved significantly over a short period (Bergslien 2013; Buxton et al. 2018; Willi et al. 2018) (Fig. 3).

Table 4 Involvement of AI-based methods at its pre-mature level for biodiversity monitoring
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Fig. 3
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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)

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Recently, a wide range of low-cost yet powerful sensors, microphones, and cameras have become available, giving aid to alleviating the problem of collecting data. Such extensive data collections from the said technologies fueled DL models to learn more patterns that generated solutions to better monitor and manage biodiversity. The common uses include automated recognition, classification, and detection of people (Kim and Moon 2016), animals (Verma and Gupta 2018), plants (Saleem et al. 2019), fish (Jalal et al. 2020), and even insects (Xia et al. 2018) based on their sound or image (Christin et al. 2019). Even with DL’s promising capabilities, it still exhibits some caveats that limit its full potential in biodiversity monitoring, specifically in real time. Monitoring wildlife through video became an exponential and popular recent development that improved interpretability with less comprehension to researchers and the like (Chen et al. 2019). However, it became difficult and expensive due to the challenging deployment of capable computers or capture devices to perform the task (Willi et al. 2019). While operating with DL models in urban areas is relatively easy due to the availability of sufficient data on infrastructure, functioning in remote areas still relies on post-monitoring systems (He et al. 2016; Zhang et al. 2019a).

Researchers are also on for finding more efficient data collection techniques that will require less computational cost and fewer complexes. Currently, the computer on a hardware basis still rigorously improves and becomes more affordable and independently deployable. With that said, DL can become more efficient and reliable over time that can produce real-time wildlife monitoring in remote areas through a more visual aspect like videos without much constraint from the limited infrastructure.

Challenges and future prospects

Approximately, 1 million of the 10 million species that exist in the world are threatened with extinction (Bawa et al. 2020). Besides monitoring tools, a combination of efforts from varied disciplines will be essential for the safeguard of individual species and biodiversity as a whole. Computer model-based technologies like the GIS, RADAR, remote sensing, and LiDAR are actively used for the monitoring of habitats, state of threats, land uses, and conversion. Molecular approaches such as Mitochondrial DNA (Cyt b), SNPs, RFLP, microsatellites, etc. are also playing a pivotal role in identifying, tracking, and determining the impact of anthropogenic and environmental factors on wildlife (Krestoff et al. 2021; Gouda et al. 2020; Ridley et al. 2020). However, many of these techniques face challenges in form of cost-efficiency and expert handling and have single or limited focal species at the ecosystem level. Some of the possible changes and prospects in biodiversity monitoring systems that can be implemented in near future on broader aspects are discussed below.

The science of chorology with advances in GIS and remote sensing techniques in recent times has better presented the landscape as a functional unit for biodiversity management. Visualizations of spatial-temporal changes and development of biotic and abiotic threats to species also known as “threat maps” emerged as multipurpose techniques for the implementation of conservation activities at the ground level (Ridley et al. 2020). InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) is a newly develop modeling software with set parameters for screening and quantification of ecosystem services such as carbon stock, changes in land use, landscape, forest cover, etc. SolVES is a modern-day ArcGIS-dependent tool that provides the user with easy access to several functions of the Ecosystem Services (ES), human perceptions associated with social and cultural beliefs, socio-economic values, usage of resources, etc. even without conducting questionnaires or other ground surveys of the local people and other stakeholders (Neugarten et al. 2020).

ARIES (ARtificial Intelligence for Ecosystem Services) is a series of algorithm processes which are generated through detecting or recognizing and keeping the track of living systems. It is a software-based platform that solves complex and arduous social or bio-geographical dimensions by integrating biodiversity data (Silvestro et al. 2022). It has been successfully tested for carbon emission, climate change, water levels, and ethnic/recreational values (Bagstad et al. 2018). Costing Nature is another easy-to-use rapid and reliable web-based technique used for screening protected areas, land use and land cover (LULC), trends of habitation, biodiversity assessment, and possible future threats using global database. It has been used for testing ES for timber, fuel wood, grazing/fodder, and non-wood forest products (Thessen 2016; Dominguez-Morales et al. 2021; Neugarten et al. 2018).

As rightly pointed out by Malavasi (2020), biodiversity maps are always selective and do not necessarily display all values that are known about any given region or ecosystem. They are often inevitably affected by personal views or scientific blindness and it is therefore important to strive and rate maps not only in terms of scientific accuracy but also on their “viability.” The use of Public Participation Geographic Information Systems (PPGIS) over conventional screening systems can act as a bottom-up approach to empower concern agencies about the threats and conservation priorities by providing visual tools. Similarly, the use of a counter map can prove as a possible substitute for mitigating the loss of biodiversity in a more “systemic” manner (Schägner et al. 2013; Malavasi 2020).

Genomics models and concepts are widely applied for biodiversity sustenance, from ideal seed selection for preservation to assessing the degree of impact at community-level effects. The concept of population genomics has provided valuable information on population size, demographic history, ability of the populations to evolve and adapt to the changing environment, etc. (Miraldo et al. 2016; Hu et al. 2020, 2021; Hohenlohe et al. 2021). They have been able to successfully develop large sets of markers that increase the ability to detect and quantify low levels of hybridization or admixture. Techniques such as intron sequences with assistance from Transcriptome Ortholog Alignment Sequence Tools (TOASTs), Next-Generation Sequencing (NGS), and Comparative Anchor Tagged Sequences (CATs) may represent a good proxy to assess functional adaptive potential or functional diversity in future genomic studies (Forcina et al. 2021).

Conclusion

With continuous advances in technology, more precise and reliable techniques have been designed for biodiversity conservation. However, association mapping and expanding knowledge on “omics” will help in identifying morphological traits and bring together intellectual minds to a platform for developing advanced gene traits. It also helps identify high biodiversity conservation priority areas or hotspots. Working closely with international agencies like the Convention on Biological Diversity (CBD) and UN Framework Convention on Climate Change (UNFCCC) and achieving its targets will be important for the conservation of biodiversity on the planet. Lastly, it is the human who understands the importance of coexistence and cohabitation with other forms of living beings that will help implement conservation measures and create a sense of protecting the ecosystem. Therefore, it is suggested that a combination of sophisticated monitoring methods including system-based smart techniques, transformative smart technologies, remote sensing, geographical information system, and artificial intelligence in combination with molecular approaches will smartly keep the track of living organisms and will help in biodiversity conservation and restoration.