Detectability of the global weed Hypochaeris radicata is influenced by species,environment and observer characteristics

Aims To determine the detectability of a global weedy perennial weed Hypochaeris radicata and its relationship with five common observer, species and environmental variables.Methods Trained independent observers conducted time-limited repeat surveys of H. radicata during autumn in an endangered grassy box-gum woodland ecosystem in south-east Australia. Single-species single-season site-occupancy modelling was used to determine if detectability of H. radicata was altered by five covariates, observer, litter height, grazing, maximum plant height and flowering state.Important findings Detectability for H. radicata varied significantly with observer, litter height, plant maximum height and flowering state, but not with grazing. Despite significant observer-specific variation, there was a consistent increase in detectability with plant height and when plants are in flower for all observers. Detectability generally decreased as litter height increases. Perfect or constant detection rates cannot be assumed in plant surveys, even for easily recognizable plants in simple survey conditions. Understanding how detectability is influenced by common survey variables can help improve the efficacy of plant monitoring programs by quantifying the extent of uncertainty in inferences made from survey data, or by determining optimal survey conditions to increase the reliability of collected data. For plants with traits similar to H. radicata, surveying when most plants are at maximum height or in flower, increasing search intensity when litter levels are high and minimizing observer-related heterogeneity are potentially simple and effective ways to reduce detection errors. We speculate that detection rates may be lower, more variable and involve additional covariates when surveying during the peak flowering spring season with the presence of more warm season and taller annual species.     

Factors Affecting Detection of Burrowing Owl Nests During Standardized Surveys

Abstract: Identifying causes of declines and evaluating effects of management practices on persistence of local populations of burrowing owls (Athene cunicularia) requires accurate estimates of abundance and population trends. Moreover, regulatory agencies in the United States and Canada typically require surveys to detect nest burrows prior to approving developments or other activities in areas that are potentially suitable for nesting burrowing owls. In general, guidelines on timing of surveys have been lacking and surveys have been conducted at different times of day and in different stages of the nesting cycle. We used logistic regression to evaluate 7 factors that could potentially affect probability of a surveyor detecting a burrowing owl nest. We conducted 1,444 detection trials at 323 burrowing owl nests within 3 study areas in Washington and Wyoming, USA, between February and August 2000–2002. Detection probability was highest during the nestling period and increased with ambient temperature. The other 5 factors that we examined (i.e., study area, time of day, timing within the breeding season, wind speed, % cloud cover) interacted with another factor to influence detection probability. Use of call-broadcast surveys increased detection probability, even during daylight hours when we detected >95% of owls visually. Optimal timing of surveys will vary due to differences in breeding phenology and differences in nesting behavior across populations. Nevertheless, we recommend ≥3 surveys per year: one that coincides with the laying and incubation period, another that coincides with the early nestling period, and a third that coincides with the late nestling period. In northern latitudes, surveys can be conducted throughout the day. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):688–696; 2008)     

Comparison of point counts and territory mapping for detecting effects of forest management on songbirds

Point counts are commonly used to assess changes in bird abundance, including analytical approaches such as distance sampling that estimate density. Point‐count methods have come under increasing scrutiny because effects of detection probability and field error are difficult to quantify. For seven forest songbirds, we compared fixed‐radii counts (50 m and 100 m) and density estimates obtained from distance sampling to known numbers of birds determined by territory mapping. We applied point‐count analytic approaches to a typical forest management question and compared results to those obtained by territory mapping. We used a before–after control impact (BACI) analysis with a data set collected across seven study areas in the central Appalachians from 2006 to 2010. Using a 50‐m fixed radius, variance in error was at least 1.5 times that of the other methods, whereas a 100‐m fixed radius underestimated actual density by >3 territories per 10 ha for the most abundant species. Distance sampling improved accuracy and precision compared to fixed‐radius counts, although estimates were affected by birds counted outside 10‐ha units. In the BACI analysis, territory mapping detected an overall treatment effect for five of the seven species, and effects were generally consistent each year. In contrast, all point‐count methods failed to detect two treatment effects due to variance and error in annual estimates. Overall, our results highlight the need for adequate sample sizes to reduce variance, and skilled observers to reduce the level of error in point‐count data. Ultimately, the advantages and disadvantages of different survey methods should be considered in the context of overall study design and objectives, allowing for trade‐offs among effort, accuracy, and power to detect treatment effects.     

Distance models as a tool for modelling detection probability and density of native bumblebees

Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees (Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect counts (“transect counts”) at 47 early‐successional forest patches in Pennsylvania. Our HDS models indicated that detection probabilities of Bombus spp. were imperfect and varied with survey‐ and site‐covariates. Despite being conspicuous, Bombus spp. were not reliably detected beyond 5 m. Habitat associations of Bombus spp. density were similar across methods, but the strength of association with shrub cover differed between HDS and net counts. Additionally, net counts suggested sites with more grass hosted higher Bombus spp. densities whereas HDS suggested that grass cover was associated with higher detection probability but not Bombus spp. density. Density estimates generated from net counts and transect counts were 80%–89% lower than estimates generated from distance sampling. Our findings suggest that distance modelling provides a reliable method to assess Bombus spp. density and habitat associations, while accounting for imperfect detection caused by distance from observer, vegetation structure, and survey covariates. However, detection/non‐detection data collected via point‐counts, line‐transects and distance sampling for Bombus spp. are unlikely to yield species‐specific density estimates unless individuals can be identified by sight, without capture. Our results will be useful for informing the design of monitoring programs for Bombus spp. and other pollinators.     

MLE and Bayesian inference of age-dependent sensitivity and transition probability in periodic screening

This article extends previous probability models for periodic breast cancer screening examinations. The specific aim is to provide statistical inference for age dependence of sensitivity and the transition probability from the disease free to the preclinical state. The setting is a periodic screening program in which a cohort of initially asymptomatic women undergo a sequence of breast cancer screening exams. We use age as a covariate in the estimation of screening sensitivity and the transition probability simultaneously, both from a frequentist point of view and within a Bayesian framework. We apply our method to the Health Insurance Plan of Greater New York study of female breast cancer and give age-dependent sensitivity and transition probability density estimates. The inferential methodology we develop is also applicable when analyzing studies of modalities for early detection of other types of progressive chronic diseases.     

施氮对干旱区黑果枸杞光合-CO_2响应及药效成分的影响

为探明干旱区栽培黑果枸杞叶片光合-CO_2响应特征及果实药效成分对施氮量的响应,该试验设置5个不同施氮(尿素)水平处理(0、50、100、150和200 g·株~(-1)),测定各处理植株在不同CO_2浓度下叶片净光合速率(P_n)、蒸腾速率(T_r)、气孔导度(G_s)、水分利用率(WUE)、胞间CO_2浓度(C_i)和果实中总多糖、总黄酮、花色苷、原花青素的含量,并通过直角双曲线修正模型拟合得到羧化速率(η)、最大净光合速率(P_(nmax))、光呼吸速率(R_p)、CO_2补偿点(CCP)和CO_2饱和点(CSP)等参数。结果表明:(1)适量施氮对黑果枸杞叶片η、P_(nmax)、R_p、CCP和CSP有明显的影响。当施氮量为100 g·株~(-1)时,叶片η和R_p最大、CCP最小;施氮量为100～150 g·株~(-1)时,叶片CSP最大;施氮量为150～200 g·株~(-1)时,叶片P_(nmax)最大。(2)适量施氮对黑果枸杞果实中主要药效成分含量有明显的影响。施氮量为200 g·株~(-1)时,果实总多糖含量最大;施氮量为150～200 g·株~(-1)时,总黄酮含量最大;施氮量为50～150 g·株~(-1)时,花色苷含量最大;施氮量为100～200 g·株~(-1)时原花青素含量最大。综合分析发现,干旱区栽培黑果枸杞叶片η、R_p、CCP、CSP及果实多糖和总黄酮含量以每年施氮100～150 g·株~(-1)时最佳,其叶片P_(nmax)和果实花色苷和原花青素含量以每年施氮150～200 g·株~(-1)时最佳。