Using the Wildlife Picture Index to measure ecosystem health and connectivity

Background/Question/Methods

Ecosystem health and how it is affected by climate change, landscape connectivity, and implementation of restoration activities can be measured by monitoring the top trophic levels (mammals) through the Wildlife Picture Index (WPI) (O’Brien 2010). We established two WPI arrays in Northern California to meet the following objectives: 1) establish baseline occupancy estimates for a suite of representative species to assess ecosystem health; 2) use occupancy estimates to establish the functionality of cores and corridors in the region; and, 3) monitor trends in biodiversity, abundance of key species and community composition as indicators of long term viability of ecosystem integrity.  20 sq km camera arrays were established at the 3,120-acre Pepperwood preserve (PWD) and at the 3,370-acre Modini Mayacamas Preserves (MMS) located 9km apart in the Mayacamas Coast Range of Sonoma County, CA.  Both arrays were set at a scale of a 1km grid and consistently maintained for proper functioning. We used single season occupancy analysis to generate occupancy estimates for each species (Hines 2006).   The location of our sampling efforts make the results relevant to testing regional connectivity and linkages models, including  Critical Linkages: Bay Area and Beyond  and The Mayacamas Connectivity Report(Merenlender et al. 2010). 

Results/Conclusions

Season 1 (S1) and 2 (S2) from our Pepperwood (PWD) Array resulted in 1,505 and 1,673 camera trap nights, respectively, and Season 4 (S4) from the Modini Mayacamas Preserves Array (MMS) resulted in 1,575 camera trap nights.  Seasonal occcupancy estimates by species ranged from less than 0.1 (racoon) to greater than 0.9 (mule deer).  Rates for black bear ranged from 0.2-0.3 and rates for puma ranged from 0.2-0.6.  We also generated occupancy estimates for taxonomic groups to compare trophic levels (small mammals, large ungulates, mesocarnivores, and large carnivores). The value of implementing the WPI is 1) to provide baseline occupancy estimates and over time, trends in biodiversity, individual species, and health of trophic levels; 2) to test hypotheses regarding how PWD and MMS function as a core or corridor for San Francisco Bay wildlife; and 3) to assess how environmental drivers and management strategies in the region may be adversely or positively affecting wildlife over time. By conducting sustained landscape-level WPI monitoring, we can generate statistically reliable estimates of abundance variability. Thus far we have found a robust assemblage of species within our study areas indicating ecosystem health. Next steps will include using the WPI to test regional connectivity models.