Power to the people: Analysis of occupancy models informed by local knowledge. Issue 8 (7th July 2022)

• Record Type: Journal Article
• Authors: Brittain, Stephanie; Rowcliffe, Marcus; Earle, Samantha; Kentatchime, Fabrice; Kamogne Tagne, Cedric Thibaut; Milner‐Gulland, E. J.
• Is Part Of: Conservation science and practice. Volume 4:Issue 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-07
• Subjects: Africa -- bushmeat -- camera traps -- detection -- interdisciplinary -- local ecological knowledge -- monitoring -- occupancy analysis -- power analysis -- tropical forests; Biodiversity conservation -- Periodicals; Biodiversity conservation; Periodicals; 333.951605
• DOI: 10.1111/csp2.12753
• Languages: English
• ISSNs: 2578-4854

Abstract

Abstract: Power analyses help to improve the cost‐effectiveness of monitoring strategies for wildlife populations, but rarely account for variation in detection probability, affecting the power of data to detect trends in occupancy. We explore the power of occupancy models informed by two locally‐informed methods (interviews and daily diaries) to detect changes in occupancy for 14 mammal species hunted for wild meat within a community forest in Cameroon. This is the first study to use the formula developed by to compare power between locally‐informed methods and camera traps, and identify the monitoring strategies best suited to different species. Comparable effort is required between the three methods to detect 50% as 80% change in occupancy, except where occupancy is less than 0.13 (diary data), 0.03 (camera), or 0.6 (interviews). Overall, where occupancy <0.54, 200 sites and four repeat visits were required to detect at least a 30% change in occupancy. Achieving power to detect any level of change useful for conservation planning is often not viable for projects with small budgets and for species with very low detection rates. However, some species of conservation importance (e.g., gorilla, chimpanzee) are better detected and as such could be monitored using data collected in collaboration with local communities.