What challenges arise when using GIS data for wildlife management?

1 Data availability

One of the main challenges of using GIS data for wildlife management is finding and accessing relevant and reliable data sources. Wildlife data can come from various sources, such as satellite imagery, remote sensing, field surveys, GPS tracking, citizen science, and historical records. However, not all of these sources are easily available, updated, or standardized. For example, some data may be restricted by copyright, privacy, or security issues, while others may be outdated, incomplete, or inconsistent. To overcome this challenge, wildlife managers need to identify and evaluate the data sources that suit their objectives, collaborate with other stakeholders, and use open and interoperable data platforms.

2 Data quality

Another challenge of using GIS data for wildlife management is ensuring the accuracy, precision, and validity of the data. Data quality can be affected by various factors, such as measurement errors, sampling biases, spatial resolution, temporal frequency, and data processing methods. For example, some data may have high uncertainty, noise, or distortion, while others may have low coverage, resolution, or frequency. To overcome this challenge, wildlife managers need to assess and document the quality of the data, apply appropriate quality control and correction techniques, and use statistical and spatial analysis methods to reduce errors and biases.

3 Data integration

A third challenge of using GIS data for wildlife management is integrating and harmonizing data from different sources, formats, scales, and projections. Data integration is essential for creating comprehensive and consistent maps and models of wildlife distribution, movement, and habitat. However, integrating data can be difficult, as different data sources may have different attributes, metadata, standards, and conventions. For example, some data may have different spatial or temporal units, coordinate systems, or data models, while others may have different attribute names, values, or definitions. To overcome this challenge, wildlife managers need to use data conversion and transformation tools, adopt common data standards and formats, and use metadata and ontologies to describe and link the data.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?