Effective sampling area is a major driver of power to detect long‐term trends in multispecies occupancy monitoring

Ecosphere

Martha M. Ellis¹  Katie M. Moriarty²  Jody M. Tucker³  Jessie D. Golding⁴ 

[1] Department of Mathematics Montana State University 1156‐1174 South 11th Street Bozeman Montana59715USA;USDA Forest Service, Pacific Northwest Research Station 3625 93rd Avenue Olympia Washington98512USA;USDA Forest Service, Pacific Southwest Region 1323 Club Drive Vallejo California94592USA;USDA Forest Service, Rocky Mountain Research Station 800 East Beckwith Avenue Missoula Montana59801USA;

关键词: bioacoustic monitoring;    carnivore;    effective area;    Martes caurina;    multispecies monitoring;    non‐invasive monitoring;   

DOI  :  10.1002/ecs2.3519

来源: DOAJ

【 摘 要 】

Abstract Occupancy‐based monitoring has become an important tool in wildlife conservation and management. Nonetheless, meeting occupancy modeling assumptions and providing biologically accurate information are difficult tasks over long time periods, large areas, or when monitoring multiple species. In occupancy modeling frameworks, derived grids are commonly used to divide landscapes into discrete units. Grid sizes that match the home range size of the species of interest are considered optimal, but this practice is complicated as home range size may vary by sex, habitat quality, or among species. Additionally, studies often assume their survey methods sample an entire grid cell when the actual effective sampling area may be much smaller. The effect of reduced effective sampling area on occupancy estimation has received little attention to date, despite being flagged as a critical issue. In this study, we assessed (1) how the relationship between effective area, home range size, and grid size affects power to detect trends in occupancy; (2) how varying the sampling design factors of effective area, duration, detection probability, and resurvey interval influence monitoring efficiency; and (3) determine whether a single sampling design can simultaneously detect declines in two species with different home range sizes. We used a spatially explicit simulation framework to create biologically realistic declining populations over 10 yr and assessed statistical power to detect known declines using occupancy modeling. We found that effective area and detection probability had the greatest influence on statistical power. We could not reliably detect declines when detection probability was low or when effective sampling area was <1/4 cell. We conclude that failing to account for effective area less than the cell size will result in overestimation of statistical power. Our simulations suggest occupancy models can detect declines for two species with different home range sizes using the same grid cell size under certain conditions, for instance, surveying >25% of the landscape, ≥25% effective area, and fixed sampling locations. Further, increasing resampling interval greatly increased monitoring efficiency. Our results show monitoring planning requires explicit consideration of effective sampling area and methods with sufficient detectability to detect population declines.

【 授权许可】

Unknown