Integrating occurrence and detectability patterns based on interview data: a case study for threatened mammals in...

Area of Science:

• Ecology
• Conservation Biology
• Wildlife Management

Background:

• Occurrence models are vital for estimating species ranges, understanding species-landscape relationships, and guiding global conservation efforts.
• Accurate species detection, accounting for imperfect detection and potential false positives, is crucial for reliable ecological assessments.
• Equatorial Guinea's Río Muni harbors threatened mammal species requiring focused conservation strategies.

Purpose of the Study:

• To estimate occurrence and detection probabilities for seven threatened mammal species in Río Muni, Equatorial Guinea.
• To identify key environmental covariates influencing species distribution and detection.
• To pinpoint priority areas for conservation of threatened mammals within the region.

Main Methods:

• Conducted a large-scale survey in 2010 across 225 sites in Río Muni.
• Utilized hunter interviews to gather presence/absence data for golden cat, leopard, forest elephant, forest buffalo, western gorilla, chimpanzee, and mandrill.
• Applied single-season occupancy models and advanced models incorporating false positive errors for robust data analysis.

Main Results:

• Golden cat and leopard exhibited the lowest occurrence rates; primates showed the highest.
• Human settlements negatively impacted the occurrence of all studied species except western gorillas.
• The southern region of Río Muni demonstrated the highest occurrence rates for the surveyed threatened mammals.

Conclusions:

• Southern Río Muni is identified as a critical area for implementing conservation strategies for threatened mammals.
• Conservation planning must consider the negative effects of human settlements on species occurrence.
• Accurate occupancy modeling, including false positive error, enhances the reliability of conservation assessments for threatened species.