Current technologies and interdisciplinary research on biodiversity conservation

Biodiversity conservation is highly important in the current environmental protection. Besides the science and politics analysis, the scientists also invent the novel technologies to support the biodiversity conservation. This paper examines the important technologies in the biodiversity conservation, including big data, citizen science, CRISPR, geographic information system, automatic data capture system, and light detection and ranging.


Introduction
In contemporary environmental protection, biodiversity conservation is an important task. For biodiversity conservation, scientists and policymakers have made many efforts, including performing important scientific research, making laws and policies, applying efficient governance models, and conducting high-quality protection education. This paper focuses on an indispensable aspect: the contemporary biodiversity technologies.
Biodiversity technologies can enhance biodiversity conservation and require support from interdisciplinary research. Scientists in different disciplinaries need to cooperate with each other to jointly develop novel technologies. This paper introduces the most important biodiversity technologies of this century, including big data, citizen science, CRISPR, geographic information system (GIS), automatic data capture system (ADCS), and light detection and ranging (LiDAR) ( Table 1).  (Campbell andWynne 2011, Shan andToth 2018)

Big data
Big data is an information asset with massive amounts of data, high growth rates, and complex structures (Sagiroglu and Sinanc 2013). Big data, with its new processing methodologies, has strong decision-making power, insightful discovery ability, and process optimization capabilities. Big data has the characteristics of 4V: Volume, Velocity, Variety, and Veracity (Zikopoulos and Eaton 2011). Big data cannot be captured, managed, and processed with traditional software tools within a short time frame. Big data has been widely used in achieving sustainable development, such as sustainable cities and communities, responsible consumption and production, climate action, and life quality improvement.
There will be several opportunities and challenges for the application of big data in biodiversity conservation (Koureas, Hardisty et al. 2016):

Opportunity 1: Integrating biodiversity research
There are increasing numbers of scientists using big data to enhance biodiversity conservation (Menon 2014, Arts, van der Wal et al. 2015, Franklin, Serra-Diaz et al. 2017. Biodiversity conservation requires a novel methodology that integrates (1) genes, genomes, phylogeny, (2) biodiversity conservation science, (3) taxonomy and systematics, (4) agro-biodiversity, (5) evolutionary biology, and (6) population and community ecology. After mobilizing and integrating different data, the big data system can contribute to experiments, observations, models, and processes that will further support biodiversity conservation (see Figure 1). For example, big data was used for bioinformatics (from big data) to forecast biodiversity conservation and ecology (Menon 2014). Based on big data, scientists used deep learning to achieve large-scale biodiversity monitoring (Klein, McKown et al. 2015).

Figure 1 Schematic illustration showing big data integrating the different aspects of information (left) and contributing to experiments, observations, models, and processes (right) that further support biodiversity conservation.
Franklin and Serra-Diaz used big data to examine the impact of global change on plant communities (Franklin, Serra-Diaz et al. 2017). In this study, the authors collected data from environmental maps, species occurrence records, community compositions, and species traits. After collecting the data, the authors set up a spatially explicit model and predicted the impact of global change on plant communities. This paper successfully provided more meaningful insights into global plant distribution.
A research group mobilized and integrated big data in studies of spatial and phylogenetic patterns of biodiversity (Soltis and Soltis 2016). The authors discussed integrating biodiversity science, systematics, ecology, and evolution. This paper emphasized the importance of an integrative method. With support from the integrative method, people can now make biodiversity projections to provide important data for scientists, the public, and policymakers.
Opportunity 2. Data is everywhere and is produced at an increasing rate.
Data is everywhere and is produced at an increasing global internet traffic rate. That means there are more data and faster data transfers that can support big data and its contribution to biodiversity conservation.

Opportunity 3. Databases can be organized for better usage
Big data can now be organized in a better way. For example, there are many organized novel public databases (e.g., the National Center for Biotechnology Information database, the Genbank). The databases provide much information and support research all over the world. Scientists can share their data via publication and easily download the data they want for their studies. As a result, the novel databases support big data and its aid to biodiversity conservation.

Opportunity 4. Dark data is important, due to its volume
Of the available data, 80% is dark data, or data that has not been published, and 20% is published and discoverable data in scientific research (Cisco 2017). With the technology from big data, scientists might have better access to information from the dark data. In this case, scientists would have more available data and perform better research in biodiversity conservation.

Challenge 1. Mobilizing data at all scales
It is difficult to mobilize biological data at all scales quickly, efficiently, and costeffectively. There are vast quantities of data. For instance, there are 3 billion specimens in collections worldwide. In the biodiversity literature, there are more than 3,000 million pages.

Challenge 2. Linking and aggregating data at different scales
There is different data at different scales: 50 thousand community data files (e.g., scratchpads), 5 million national data files (e.g., Natural History Museum's Data Portal), and 500 million global data files (e.g., Global Biodiversity Information Facility Data Portal). The data is in different sizes and different formats. There is a lot of work to be done in linking and aggregating different data.
Challenge 3. Synthesizing data (e.g., modelling human pressures on biodiversity) There are large, linked biodiversity datasets. These are part of a clear, singular, longterm vision that biodiversity data can contribute to. Based on the synthesized data, big data has potential applications in identifying trends, explaining patterns, and making predictions. Furthermore, big data can contribute to real-time alerts, the creation of an suitable policy tool, and modelling the biosphere.

Big data example: using Wikipedia to quantify cultural interest in species
Based on big data, scientists used Wikipedia to quantify cultural interest in species (Mittermeier, Grenyer et al. 2018). Considering that people use Wikipedia to study popular science and knowledge, the authors studied around 60,000 vertebrates via nearly 300 language editions of Wikipedia. Based on the paper, the authors found that public interest (a large proportion of all pageviews) may follow species distribution (people pay more attention to the animals around them), body size (people pay more attention to large animals), and diet (people pay more attention to animals with a certain diet). As a result, only a small number of species (e.g., lion, wolf, tiger, panda, whale, polar bear, and shark) receive a large proportion of public interest. In another paper, the same group used Wikipedia to explore the cultural importance of global reptiles (Roll, Mittermeier et al. 2016). The authors used data of the page views to understand the global-scale patterns of human interest. The most popular species (in the top 5% of page views) were attractive animals, those which were brightly coloured, or those which were morphologically close to us. Some other species that do not have these characteristics are highly important in the ecosystem or are classified as endangered on the International Union for Conservation of Nature (IUCN) Red List, and they do not receive enough public attention and protection. This trend might be very dangerous to the ecosystem and to biodiversity conservation work.

Modern citizen science
Citizen science refers to a large number of amateur scientific enthusiasts who have not received official professional training. They participate in scientific research through network organizations. Based on internet development in recent years, citizen science has made more information available from amateur scientific enthusiasts and has played an increasingly important role. Citizen science brings happiness and fun to science enthusiasts. It is also able to support professional science work, especially in biodiversity conservation.

eBird
A previous paper examined citizen science supporting biodiversity conservation (Devictor, Whittaker et al. 2010). In this paper, the authors examined famous citizen science projects (e.g., eBird, UK Butterfly Monitoring Scheme, French Garden Butterfly Monitoring, Appalachian Mountain Watch, and Big Garden Bird Watch). The eBird was constructed by the Cornell Lab with the purpose of bird observation and conservation (Sullivan, Wood et al. 2009). The eBird founders constructed a platform

French Garden Butterfly Monitoring
French Garden Butterfly Monitoring was a system based on citizen participation (Henry, Manil et al. 2005), in collaboration with a major national gardening firm (Jardinerie

CRISPR/Cas 9 on de-extinction work
Although de-extinction work is still in its infancy, CRISPR/Cas9 might be crucial to any realistic de-extinction. In recent years, de-extinction has started to move from science imagination to a real laboratory work of discussion, research, and planning (Sherkow and Greely 2013).
In previous years, researchers normally applied back-breeding or cloning in attempts at de-extinction work (Sherkow and Greely 2013). CRISPR/Cas9 might offer more possibilities. This paper uses the mammoth as an example to illustrate the possible steps for de-extinction efforts (Brand 2014, Shapiro 2015. First, scientists might obtain ancient DNA and sequence the ancient DNA to obtain its genetic information. Second, based on the genetic information, the scientists can manually create the genome of the extinct animal (e.g., mammoth) via CRISPR/Cas9 genome editing. Third, the scientists can apply the cloning technique to put the genome into a cell and activate the cell. Although de-extinction is a very difficult and long-term process, scientists believe it will be possible to realize de-extinction through CRISPR/Cas9 in the future.

Geographic Information System (GIS)
GIS is a specific and indispensable spatial information system in current biodiversity conservation. GIS is a technical system that collects, stores, manages, calculates, analyses, displays, and describes the geographical data in the earth's surface. GIS can analyse and process spatial information. GIS technology integrates the unique visual effects and geographic analysis functions of maps with general database operations (Maliene, Grigonis et al. 2011).
GIS has various applications, such as real estate, public health, crime maps, national defence, natural resources, landscape architecture, archaeology, community planning, transportation, and logistics. The GIS system also supports the Global Positioning System (GPS) service. For example, it enables people to use GPS to find nearby restaurants, gas stations, and schools. In biodiversity conservation, GIS can support the scientists find and protect the habitats of endangered species.
In applications, there is a well-known software-ArcGIS. ArcGIS provides users with a scalable, comprehensive GIS platform. ArcGIS includes many components: from finegrained objects (such as individual geometric objects) to coarse-grained objects (such as map objects interacting with ArcMap documents). ArcGIS is extremely extensive and it integrates comprehensive GIS functions (e.g., geostatistical analysis, spatial statistical analysis, or visualization of species distribution) for users (Johnston, Ver Hoef et al. 2001, Wong andLee 2005).

Automatic Data Capture System (ADCS)
The automatic data capture system (ADCS) has provided new opportunities for biodiversity conservation. Decades ago, scientists needed to obtain data through fieldwork. Now, scientists have ADCS, and they can more easily obtain data and perform analyses. To describe the basic concepts and advantages of the ADCS, this paper gives a simple example: the ADCS can be used to examine the impact of industry on ecology and policy response performance (Bradley, Merrifield et al. 2019).
The traditional system was paper-based (see Figure 2a). First, the industry (e.g., fisheries) provided paper logs. Then, scientists aggregated all paper logs and organized them into files. The files were sent to a research institute for further analysis. Finally, the government decided upon and implemented a new policy. Due to the complicated communications between each unit, this traditional system was inefficient and timeconsuming.
The ADCS is based on computers and the internet (see Figure 2b). The industry provides information via the internet. The information is then automatically collected and analysed. After that, the processed data is sent to the government and research institutes. With cloud-based integrated data sharing, the government and research institute examine the data and design a new policy together. Because the data is relatively unified (computerized and networked), the ADCS is much more efficient than the traditional system.

Figure 2 A simplified comparison between (a) the traditional system and (b) the ADCS
in the process of acquiring data and making decisions. The idea and information were from a previous paper (Bradley, Merrifield et al. 2019), and the figure was replotted by the author of this paper.

LiDAR
Light Detection And Ranging (LiDAR) is a remote sensing approach that use the laser to measure ranges to the Earth (Campbell and Wynne 2011). LiDAR can measure the distance by illuminating the target with laser light and a sensor (see Figure 3). The differences in laser return times and wavelengths can be used to calculate the digital   LiDAR has been widely used in many fields, including the terrestrial laser scanning (TLS) for the trees and forests , Lau, Martius et al. 2019, fish biomass estimation in the marine protected areas (Wedding, Friedlander et al. 2008), airborne laser swath mapping (ALSM) (Kim, Arrowsmith et al. 2006), and autonomous vehicle (e.g., self-driving car) (Lim and Taeihagh 2019). This paper introduces several applications of LiDAR during biodiversity conservation.

LiDAR in the forest management
LiDAR is a novel technique which is used in forest management.
To better perform efficient biodiversity conservation, scientists need to know the current status of the forests. Traditionally, to understand the status, scientists had to scientists and support policymakers in the conservation decision.

Tree structure analysis via LiDAR
Previously, if the scientists would like to measure the tree size and understand why the tree grows so tall (e.g., decided by hydraulics or wind speed), the scientists had to spend a lot of time to perform the fieldwork and collect the data (e.g., DBH, height, weight, or biomass). Especially, it was very difficult for scientists to manually measure large trees. Furthermore, some trees cannot be measured directly because some areas were inaccessible (e.g., no roads). These points above were difficulties during the measurement.  Lau et al. 2018).

LiDAR in marine conservation
Marine biodiversity conservation is normally based on the spatial approach: (1) the spatial approach is a robust tool for implementing ecosystem-based management; (2) the spatial approach is efficient in addressing research questions at the broad scales relevant to informing management. In the spatial approach, scientists require innovative techniques (e.g., LiDAR) to scale-up ecological studies to support marine conservation and management.
To better perform the spatial analysis, scientists examine the coral reef ecosystems in multi-scale seascapes. In this case, the scientists apply the LiDAR system (with the information of seascape ecology, predictive modelling, and coral reef remote sensing).
The objectives are (1) to evaluate the use of LiDAR to measure coral reef habitat complexity and (2) to design marine protected area (Pittman, Costa et al. 2010, Pittman and Brown 2011, Wedding, Lepczyk et al. 2011. For example, Simon Pittman et al. applied the LiDAR to examine the seascape of fish habitat and recommended sustainable fisheries (Pittman, Costa et al. 2010).
Furthermore, Pittman and Brown used the LiDAR to examine the fish species distributions across coral reef seascape (Pittman and Brown 2011). Lisa Wedding et al.
applied the LiDAR and successfully quantified the seascape structure (Wedding, Lepczyk et al. 2011). These studies above have enhanced scientists' understanding of marine seascape and provided policymakers with more accurate marine conservation information.