Browsing by Subject "detection probability"

Now showing 1 - 3 of 3
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    Diving-duck Productivity: Effects of Predator Management on Nest Success and Investigating Sightability-adjusted Brood-pair Ratios
    (2019-10) Johnson, Michael
    Nest success, a major component of productivity, is often used as the metric to measure the effectiveness of various management efforts aimed at increasing waterfowl productivity. Although numerous studies have proven predator reduction increases nest success for upland nesting waterfowl, less is known about its effects on the over-water nesting guild (i.e. diving ducks), which there exists no current management specifically for over-water nesting ducks. From 2015-2017 in the Prairie-Parkland Region of Manitoba, we assessed daily-survival rates of over-water duck nests in areas where efforts to reduce the local predator community were being coordinated and compared them to nearby areas where no targeted predator management occurred. Given the challenges in locating over-water nests, we also investigated an alternative method to estimate productivity using multiple rounds of surveys to derive brood-pair ratios. Brood-pair ratios have been widely used to index productivity, but biases associated with detection probabilities (the probability a pair or brood is seen during a survey) can result in underestimating abundances, especially for broods. We conducted replicate surveys to estimate detection probabilities of broods hatched from over-water nests to include in brood-pair ratios and compared productivity estimates derived from adjusted brood-pair ratios with estimates calculated from nest success on the same sites. We located and monitored 1,673 over-water nests from a variety of duck species to derive daily-survival rates and nest success estimates using Shaffer’s logistic-exposure methods and included a variety of covariates hypothesized to influence the probability a nest was successful. Nest success ranged from 14-48% across trapped and control sites, yet no overall trapping effect was observed despite numerous predators being removed from the landscape. Temporal effects such as nest-age and initiation date were influential predictors of daily-survival rates, which increased with nest-age and as the nesting season progressed. Detection probabilities for broods were estimated from 1,915 unique encounter histories using Huggin’s closed-capture methodology, which also incorporated covariates hypothesized to influence detectability. Detection probabilities were >50% for broods during all survey rounds and most heavily influenced by the percentage of the inundated wetland unobstructed for viewing broods. Sightability-adjusted brood-pair ratios for single-species were weakly correlated with nest success, however, combining all diving-duck species resulted in strong correlations between sightability-adjusted brood-pair ratios and nest success on each site-year (n = 18, R2 = 50%, P = 0.0005). Therefore, sightability-adjusted brood-pair ratios when multiple species are combined provide a useful alternative to index local diving-duck productivity when estimating nest success is unfeasible.
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    Non-lethal monitoring for endangered insects: Making inferences about imperiled bumble bees while accounting for heterogeneity in the detection process
    (2023-05) Boone, Michelle
    Insect declines are of mounting concern, yet evidence for widespread declines is limited due to a lack of standardized, long-term datasets. Furthermore, practitioners often fail to account for heterogeneity in the detection process when making inferences from survey data. As more insects are petitioned for listing under the Endangered Species Act, it is imperative that monitoring schemes implement standardized sampling protocols and adopt analytical methods that account for imperfect detection of target species during surveys. To optimize sampling for endangered insects, we must better understand the effects of biotic and abiotic factors on occupancy and detection probability of target species. In addition to understanding key factors that influence detectability of rare species to improve monitoring, it is crucial to identify habitat preferences of endangered insects to implement effective recovery and conservation planning. Bumble bees (Hymenoptera: Apidae: Bombus) are among the insect taxa with the best evidence of widespread declines. Bumble bee declines were reported in the United Kingdom as early as the 1970’s. In North America, the rusty patched bumble bee (B. affinis Cresson) was listed as federally endangered in Canada in 2012 and the United States in 2017. Franklin’s bumble bee (B. franklini Frison) was also listed in the U.S. in 2021. Additional species have been petitioned and are under consideration for listing. This has led to calls from scientists for a national bee monitoring framework to support conservation planning. For my doctoral research, I investigated the relationships between site, weather, and survey covariates with detection and occupancy probabilities for a suite of bumble bee species in Minnesota, USA, including B. affinis. I also investigated associations between habitat type and occupancy and detection probabilities. I collected bumble bee data during the summers of 2018, 2019, and 2021 for three distinct studies and used single-season, multi-species occupancy models to address my research questions.Assessing factors related to Bombus occupancy and detection probabilities is an emerging area of research, thus basic information about these relationships is sparce. For my first study, I investigated the effects of impervious surface and floral area on Bombus occupancy probability, and whether date, time of day, or observer were related to detection probabilities. I conducted roadside surveys in the seven-county metropolitan area of Minneapolis-St.Paul. This study was among the first to quantify detection uncertainty for B. affinis and to use multi-species occupancy models to draw inferences about imperiled Bombus detection. For the second study, I tested the efficacy of single-species versus multi-species occupancy models for estimating species-specific detection and occupancy probabilities. I also investigated whether bumble bees in our study system exhibited associations between occupancy probability and landscape habitat type (developed, natural, and agricultural) from surveys conducted across the state of Minnesota. I found that developed habitat had the most variable effect on Bombus occupancy probability out of the three landscape types investigated, which led to my third study, in which I delved deeper into developed landscapes. In this study, I investigated whether detection probabilities were related to adjacent habitat type (woodland, wetland, or crops) in the heterogenous, mixed-use landscape of Washington County, one of the seven metropolitan counties that serve as the human population center of Minnesota and which represents a transitional zone from a dense urban environment to a rural agricultural landscape. The results of my dissertation research provide some of the first investigations into the effects of biotic and abiotic factors on the bumble bee detection process. This information can be used to develop survey and monitoring protocols for community and species-specific monitoring. The results can also inform recovery monitoring for the endangered B. affinis or be adapted to investigate factors that influence the detection process for other imperiled insects. The finding that B. affinis occupancy probability is associated with developed areas lends support to the idea that urban areas may provide important refuge for this species in our study region. The associations between Bombus detection probabilities and survey, weather, and site covariates can be used to optimize sampling design for rare species. Taken together, this dissertation provides a framework for imperiled species monitoring that accounts for uncertainty in the detection process and can be adapted to other insect species.
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    R Code and Data Supporting: A comparison of survey method efficiency for estimating densities of Zebra Mussels (Dreissena polymorpha)
    (2023-05-25) Ferguson, Jake M; Jimenez, Laura; Keyes, Aislyn A; Hilding, Austen; McCartney, Michael A; St. Clair, Katie; Johnson, Douglas H; Fieberg, John R; jfieberg@umn.edu; Fieberg, John R
    This repository contains data and R code supporting Ferguson et al. A comparison of survey method efficiency for estimating densities of Zebra Mussels (Dreissena polymorpha).