Monitoring wildlife populations is a common practice that has been employed for decades to help assess the conservation status of individual species and to help improve management decisions. In addition, it provides important information about the status of species and their habitats.

However, monitoring of wildlife populations can be a challenging and expensive endeavor that requires careful design and data analysis to ensure useful results. It is also necessary to select the right survey methods, determine appropriate benchmarks and thresholds, and consider potential sources of error in the design of monitoring data.

The selection of a survey method depends on the specific monitoring objective and the focal species. For example, some methods may be simple (e.g., reporting by transportation agency personnel) while others require specialized equipment or training. The most efficient and economical method will be determined by the nature of the monitoring needs, budget available for equipment and personnel, and the time needed to collect data.

Using remote cameras for animal detection in road sections with wildlife crossing structures can be an effective and low-cost approach. However, there is limited data on the number of distinct animals that use the crossings, and the number of individuals in each species – a critical level of biological organization (level 1 – genes, level 2 – individuals, and levels 3 & 4 – species/populations).

Non-invasive genetic sampling is another technique that can be used to obtain information about numbers of animals using wildlife crossing structures. Similarly, DNA-based detection of hair can be an effective way to identify individual birds or mammals that use the crossings.

In addition, genetic analyses can provide state wildlife agencies with new information on how hunting practices affect populations over time. This can help them better regulate hunting of species that have mixed populations (e.g., salmon and ducks) or breed with other species to maintain more genetically diverse and economically desirable populations (Schwartz et al. 2006).

Monitoring wildlife populations at a system scale can be more complicated and requires more sophisticated analytical techniques to interpret the data. This may include a variety of quantitative and qualitative metrics that measure the health and resilience of an ecosystem as well as the impact of human factors.

Increasingly, systems-based approaches to ecological monitoring are becoming more prevalent because they integrate data at multiple scales and levels. These types of projects can be based on data from a wide range of sources, including citizen science, to provide insights into the biodiversity of an area and the relationship between human activities and wildlife in an ecosystem context.

Citizen science has become a very powerful tool for assessing biodiversity and its impacts, from local to global scales. This has prompted many government agencies and private businesses to ask citizens to assist in observing the health and status of wildlife.

The goal of citizen science is to make scientific observations that help researchers study the natural world and its biodiversity on a large spatial scale. This is a very valuable source of data for many species, especially those that have not been studied as extensively or at a high scale previously. These data can be useful for identifying changes and trends, and they can help to identify patterns that may have been overlooked in previous studies.