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03/03/2022

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How Database Management & Big Data Is Helpful In Wildlife Conservation?

Updated on: 20/05/2022

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Data management is often considered a nuisance and not given much importance unlike the sampling design, objective setting, data collection and analysis when it comes to most of the monitoring projects. However, a proper database management syststem (DBMS) is much needed and is required to be included as a critical component of any monitoring plan. Database management must also be included in the initial phase of the planning process. 

In various ways, such a system performs to be the backbone of any monitoring mission and must detail all of the steps of data collection, storage and distribution. Sound data management is so important owing to the fact that the monitoring project adapts as well as changes over time and the same is with the data as well. 

In addition to this, as most of the monitoring projects are conducted over many years, there are chances of staff change, changes in methodologies, data collection, land ownership, accessibility, methodologies, shifting technologies, improper data management can all fail to document these alterations and undermine the whole monitoring initiative. 

In this article, we will discuss how DBMS and managing your data efficiently in addition to the implementation of big data plays a great role in effective wildlife conservation. But before that we would like to offer a handy database management basics. 

Database Management: The Basics

The data that is generated from the various monitoring programs are often complicated and the protocols utilized to generate these data can alter as well as adapt over time. Accordingly, the system that is used to describe these data and the methods used to collect them must be comprehensive, flexible to changes and detailed. The process is simple - monitoring data sets are collected and entered into a database (in opposition to being stored in a filing cabinet). 

A comprehensive database must include six of the basic data deors that details the way they have been collected, measured, estimated and managed. These basic data deors ensure the long-term success of a monitoring effort as they well describe the details of the data collection and storage. 

The six necessary data deors representing an important aspect of data collection facilitating future use are:

  • What (describing the type of organism) 
  • How Many (describing the units of observation for individual organisms or colonies, presence or absence, detection or non-detection, distance measurements and relative abundance)
  • Where (describing the geographic location or geolocation at which the organism was recorded as well as what coordinate system was referenced)
  • When (describing the exact date and time of recording the event)
  • How (explaining what kind of record is represented and the various other details of the data collection protocols such as mist netting, clover trap, 5-minute point counts etc.)
  • Who (mentioning the person responsible for collecting the data)

Benefits of Digital Database & Database Management 

So what are the benefits of making use of the digital databases? Here are all the major advantages of database management systems:

  • Immediate data availability, for example, real-time online data entry.
  • Lack of the labor-intense data key-in sessions later on.
  • Automated metadata and processing.
  • Improved data sharing and data security.
  • Effective data integration.
  • Consistent and standardized data. 
  • Data that complies with the regulations.
  • Increase in the productivity of the end users.
  • Faster decision making.
  • Cleaner and more actionable data. 

Data Storage & Backup

For many of the monitoring programs, the hardcopy data are still collected irrespective of the increasing availability of the digital formats. Generally, there are three basic obstacles for the shift from the hard copies to the digital formats. In the very first, the significant amount of the historical hardcopy data remains to be digitized, say, in libraries, archives and filing cabinets. In the second, despite the fact that technological advances are making the data collection in the field more feasible, several field data are still collected in the field notebooks when the field conditions are difficult and the field site is remotely located. Third, several digital datasets are still getting printed as hardcopy for logistical and cultural reasons. 

In most of the cases, even when the data are compiled digitally, the hardcopies are collected as an important backup for many monitoring programs. These data are retained and maintained as the critical sources of information for the legacy programs that have been running for decades. As it is a hassle to create a transition from the traditional form of data storage to the digital format, but there exists several advantages why the monitoring data must be collected and stored in a digital format with the required back-up systems. 

What Is Big Data?

One of the most popular interpretations of “Big Data” points out to the extremely large data sets. The  National Institute of Standards and Technology report defined big data as:

“Extensive datasets—primarily in the characteristics of volume, velocity, and/or variability—that require a scalable architecture for efficient storage, manipulation, and analysis.” 

Some have even defined big data as “an amount of data that exceeds a petabyte—one million gigabytes.”

A third definition also exists for big data that refers to “the exponential increase and availability of data in our world.”

The big data comes from a plethora of sources, social media posts, smartphones, sensors such as utility meters and traffic signals, point of sale (PoS) terminals, consumer wearables like the fit meters, medical fields, and wildlife databases and even more.

Immense opportunities are buried deep within these data that the organizations can use to transform to their actionable insight, competitive advantage and improved decision making. By harnessing the massive power of big data, the healthcare systems can identify the at-risk patients and act upon sooner. The police departments can predict crime and take necessary actions to prevent it before it starts. The retailers can forecast better inventory for optimizing the supply-chain efficiency. 

The wildlife organizations can improve and implement new ways of sustainable wildlife conservation. The possibilities of big data are endless. But in order to fulfill the promise, the organizations are in need of the qualified data scientists or professionals bearing database management skills to extract the meaning from the heaps of data. Unfortunately, the elusive data scientists are in short supply. 

How Does The Big Data Tracking Technologies Aid In Improved Wildlife Conservation

The recent advances in the wildlife tracking techniques have enabled the large-scale data collection on the detailed movements of several animal species. The application of all of these approaches have unearthed new insights about how the animals use their environment, interact with each other and respond to the anthropogenic alterations. These were the details that were earlier impossible to explore. 

Over the past decade, the technological advances have transformed the field of movement ecology, which is the organismal movement that is from meager data discipline to the data rich discipline. This constant big data revolution is driven by the cost effective and automated wildlife tracking systems that usually generate massive and high resolution datasets that actually match the ecological context in which the animals interact with, perceive and respond to their environment. 

The other tracking technologies like the GPS, radars, computer vision systems too have the capability to produce big data and the researchers recommend viewing all of the major tracking technologies as more complementary rather than the competing alternatives. 

Another reverse GPS system going by the name “acoustic telemetry” utilizes the acoustic tags that tracks fishes and other aquatic animals underwater. Various systems of these kinds have been installed in the seas, lakes and rivers around the world that are yielding new scientific insights and important guidelines for dealing with the human imposed risks for wildlife. 

For instance, by making use of the acoustic tracking system in the European rivers and implementing the crucial database management tools, the researchers found out that the down-stream mitigating eels, a critically endangered species as well as the Atlantic salmon, changes their behavior upon encountering dams and seemingly increasing their energy expenditure and the mortality risk. 

Conclusion

Technology has grown immensely in the last two- three decades offering various out of the box facilities through its applications. Most of the sectors have seen an upliftment in the methodologies and the outcomes where Wildlife is one that even included Artificial Intelligence (AI). Collecting wildlife data, storing data in the databases and managing them throughout have become simpler with the various database management systems

Database management is a huge sphere and its implementation varies from one sphere to the other. With this the work structure of how to manage the data and how it can be used for the betterment of the particular sphere highly varies. Being said that, it is of utmost importance to understand that database management in an IT sphere will largely vary with the non-IT sphere owing to the implementations and work structure. But the meaning of the term remains unaltered.


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