Long-term, large-scale monitoring of wildlife metacommunities is needed to recognize population declines early enough to identify environmental stressors and facilitate adaptive planning. Potential outcomes include information supporting the designation of new species of conservation concern, or better yet, conservation actions that avert the need for conferring critical statuses. By surveying multiple species, declines of individual species need not be considered in isolation, but can be compared to responses of other species in the metacommunity. However, multi-species monitoring presents a variety of new challenges in terms of appropriate survey methods and analytical techniques for drawing valid ecological inferences.
In this study, I investigated several related aspects of multi-species monitoring. One theme was the role of automated survey methods (e.g., audio recorders and camera stations) that leave a permanent record and easily provide for temporal replication of surveys. I applied occupancy models to repeat surveys for addressing detection probability and providing unbiased estimates of species occurrence. Lastly, I evaluated several novel quantitative methods for comparing community properties using monitoring data.
In the first chapter I considered the effectiveness of automated recorders for monitoring common birds in California forests. I applied single-species occupancy models to 46 species using 5 years of monitoring data in which automated recorders were placed at 453 random sites across a 5.4-million-ha northern California study area. The devices were programmed to record sounds during up to 3 surveys each morning on 3 consecutive days during the breeding season when songbirds were singing from territories. Skilled interpreters reviewed these recordings to identify all species heard during each survey. With Monte Carlo simulation and results from occupancy models, I demonstrated 80% power for monitoring declines as small as 2.5% per year over 20 years for 32 species given a sampling effort of 100 new sites per year. I also determined an effective survey area radius of 30 m to 50 m for automated recorders, and showed that the devices provided similar occupancy estimates as traditional point counts despite lower survey-level detection probability.
In the second chapter I applied multi-species occupancy models to the calculation of biodiversity indices describing metacommunity organization. I used the same automated recorder data set for birds from Chapter One. Specifically, I applied simulation and Bayesian hierarchical models to demonstrate how a failure to address detection probability heterogeneity underestimates the evenness of species occupancy distributions. In models of the bird data I found that a number of species traits (migration, foraging guild, territoriality, body size) were informative in explaining detection probability. By pooling information from common species in a multi-species model, I was able to draw stronger inferences about rarer species than by modeling these species individually. Lastly, I illustrated the ecological significance of species-traits modeling and found that warbler and woodpecker occupancies were evener than for sparrows.
In the third chapter, I proposed a new quantitative method for comparing species abundance distributions. I illustrated this method using avian point count surveys from 4 research forests in California. I applied bootstrap resampling to probabilistically compare the abundances of intermediate ranks among and within species abundance distributions. I found higher abundances of intermediately-common species on 2 of the forests, and ascribed this finding to differences in forest productivity and habitat complexity leading to greater niche partitioning of resources. At the metacommunity-level, I found higher abundances of intermediately-common species for neotropical migrants compared to resident birds.
In the fourth chapter, I considered the use of baited camera stations for monitoring Pacific fisher (Pekania pennant pacifica) and other mammals. Cameras were placed at 172 randomly selected forest sites across 2.8 million ha of northwestern California. The duration of each survey was 2 to 4 weeks. I estimated regional occupancy from these data at 2 survey scales (e.g., individual sites [0.465] and pairs of sites 1.6 km apart [0.651]). I also demonstrated 80% power for monitoring declines as small as 2.0% per year over 20 years given a sampling effort of 100 new sites per year. Lastly, I calculated the median latency to first detection for 13 other species of mammals detected at > 5% of sites, showed that latency was < 6 days for 10 of these species, and argued that these results strengthen the case for expanding the use of camera traps to multi-species monitoring.
|Advisor:||Barrett, Reginald H.|
|Commitee:||Beissinger, Steven R., Bowie, Rauri C. K.|
|School:||University of California, Berkeley|
|Department:||Environmental Science, Policy, & Management|
|School Location:||United States -- California|
|Source:||DAI-B 77/08(E), Dissertation Abstracts International|
|Subjects:||Wildlife Conservation, Ecology, Statistics|
|Keywords:||Automated recorder, Biodiversity, Camera trap, Occupancy modeling, Population trend, Wildlife monitoring|
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