Detection, survival rates and dynamics of a cryptic plant, Asclepias meadii : applications of mark-recapture models to long-term monitoring studies

1. Analysis of population trajectories is central to assessing risk in populations of conservation concern. In animal studies, researchers realize that probabilities of detection of individuals are often less than one. Plants can also escape detection due to dormancy, herbivory, or observer error, but such information is rarely incorporated into population studies. 2. We monitored a population of Asclepias meadii, a rare long-lived prairie perennial. Despite standardized methods, numbers of observed plants fluctuated greatly from 1992 to 2006. Individual plants often had periods of 1-5 years between initial and final sighting when no stems were found. To determine the actual population trajectories, we estimated rates of survival and population growth using mark-recapture models. We also estimated initial and resighting probabilities of detection. In 2007, we repeated surveys to identify reasons for low detection probabilities. 3. We estimated 95% annual survival and a population growth rate of 1.023. Probabilities of initial detection were low (typically from 0.120 to 0.311 depending on prairie burn treatment), whereas average probability of detection for marked plants was 0.728. 4. Comparisons of survival estimates from 15-and 8-year data sets revealed that survival estimates decline in the final years of a multi-year period, probably due to heterogeneity in encounter histories. 5. By conducting three different surveys in 2007, we found that both herbivory over a multiple-week period and observer error contributed substantially to gaps in detection. 6. Synthesis. Probabilities of detection that are less than one complicate interpretation of population dynamics, whether of mobile animals or inconspicuous plants. Our work illustrates three general points that could apply to many plant population studies: (i) mark-recapture models may provide insights on vital rates and population trajectories despite the extreme variability in count data that can arise because of low detectability, (ii) probabilities of initial detection can be quantified and can be considerably less than probabilities of resighting, and (iii) repeated surveys can help researchers determine the degree to which dormancy, herbivory, or observer error contribute to low probabilities of detection. Consideration of these points can improve the design and analysis of monitoring programs.