Species richness estimation and determinants of species detectability in butterfly monitoring programmes

Abstract 1. Species richness is the most widely used biodiversity index, but can be hard to measure. Many species remain undetected, hence raw species counts will often underestimate true species richness. In contrast, capture–recapture methods estimate true species richness and correct for imperfect and varying detectability. 2. Detectability is a crucial quantity that provides the link between a species count and true species richness. For insects, it has hardly ever been estimated, although this is required for the interpretation of species counts. 3. In the Swiss butterfly monitoring programme about 100 transect routes are surveyed seven times a year using a highly standardised protocol. In July 2003, control observers made two additional surveys on 38 transects. Data from these 38 quadrats were analysed to see whether currently available capture–recapture models can provide quadrat‐specific estimates of species richness, and to estimate species detectability in relation to transect, observer, survey, region, and abundance. 4. Species richness over the entire season cannot be estimated using current capture–recapture methods. The species pool was open, preventing use of closed population models, and detectability varied by species, preventing use of current open population models. Assuming a closed species pool during two mid‐season (July) surveys, a Jackknife capture–recapture method was used that accounts for heterogeneity to estimate mean detectability and species richness. 5. In every case, more species were present than were counted. Mean species detectability was 0.61 (SE 0.01) with significant differences between observers (range 0.37–0.83). Species‐specific detection at time t+ 1 was then modelled for those species seen at t for three mid‐season surveys. Detectability averaged 0.50 (range 0.17–0.81) for individual species and 0.65, 0.44, and 0.42 for surveys. Abundant species were detected more easily, although this relationship explained only 5% of variation in species detectability. 6. These are important, although not entirely unexpected, results for species richness estimation of short‐lived animals. Raw counts of species may be misleading species richness indicators unless many surveys are conducted. Monitoring programmes should be calibrated, i.e. the assumption of constant detectability over dimensions of interest needs to be tested. The development of capture–recapture or similar models that can cope with both open populations and heterogeneous species detectability to estimate species richness should be a research priority.     

The landscape configuration and lethality of snare poaching of sympatric guilds of large carnivores and ungulates

Poaching of wildlife presents one of the biggest conservation challenges in the 21st century. Snaring is one of the primary means of capturing target animals. To prioritise interventions intending to reduce snaring, we describe an approach for quantifying the configuration and lethality of snares. We conducted transect surveys in Murchison Falls National Park. All the snares that we recovered were made of wire with the majority (81.0%, n = 546 of 674) deriving from vehicle tire wire. The density of snares ranged from 0.08 to 4.58 snares/km², which is the highest known density in sub‐Saharan Africa. The majority (63%) of the animals caught in wire snares were unrecovered and wasted. We found that noose width, vertical drop, wire circumference, anchor height, proportion of un‐thicketed area, grass height, distance to river and village had a significant positive relationships to lethality, while snare thickness, charms, tree DBH, thicket diameter, distance to nearest road negatively affected lethality. We recommend adopting wholistic anti‐snare countermeasures such as the human heritage‐centred conservation to empower local people. Our method illustrates the opportunity to standardise temporal and spatial measurements of snare density and configuration necessary to stop illegal wildlife poaching.     

A single phantom,a single statistical method for low-contrast detectability assessment

PurposeThe assessment of low-contrast-details is a part of the quality control (QC) program in digital radiology. It generally consists of evaluating the threshold contrast (Cth) detectability details for different-sized inserts, appropriately located in dedicated QC test tools. This work aims to propose a simplified method, based on a statistical model approach for threshold contrast estimation, suitable for different modalities in digital radiology.MethodsA home-made low-contrast phantom, made of a central aluminium insert with a step-wedge, was assembled and tested. The reliability and robustness of the method were investigated for Mammography, Digital Radiography, Fluoroscopy and Angiography. Images were analysed using our dedicated software developed on Matlab®. The Cth is expressed in the same unit (mmAl) for all studied modalities.ResultsThis method allows the collection of Cth information from different modalities and equipment by different vendors, and it could be used to define typical values. Results are summarized in detail. For 0.5 diameter detail, Cth results are in the range of: 0.018–0.023 mmAl for 2D mammography and 0.26–0.34 mmAl DR images. For angiographic images, for 2.5 mm diameter detail, the Cth s median values are 0.55, 0.4, 0.06, 0.12 mmAl for low dose fluoroscopy, coronary fluorography, cerebral and abdominal DSA, respectively.ConclusionsThe statistical method proposed in this study gives a simple approach for Low-Contrast-Details assessment, and the typical values proposed can be implemented in a QA program for digital radiology modalities.     

Survival and detectability bias of avian fence collision surveys in sagebrush steppe

We used female ring-necked pheasant (Phasianus colchicus) carcasses as surrogates for greater sage-grouse (Centrocercus urophasianus) to study factors influencing survival and detection bias associated with avian fence collision surveys in southern Idaho, USA, during spring 2009. We randomly placed 50 pheasant carcasses on each of 2 study areas, estimated detection probability during fence-line surveys, and monitored survival and retention of carcasses and their associated sign over a 31-day period. Survival modeling suggested site and habitat features had little impact on carcass survival, and constant survival models were most supported by the data. Model averaged carcass daily survival probability was low on both study areas and ranged from 0.776 to 0.812. Survival of all carcass sign varied strongly by location, and the top sign survival model included a site effect parameter. Model averaged daily survival probability for collision sign on the 2 study sites ranged from 0.863 to 0.988 and varied between sites. Logistic regression modeling indicated detection probability of carcasses during fence-line surveys for avian collision victims was influenced by habitat type and microsite shrub height at the carcass location. Carcasses located in big sagebrush (Artemisia tridentata) habitats were detected at a lower rate (0.36) than carcasses in little (A. arbuscula) and black sagebrush (A. nova) habitats (0.71). Increasing shrub height at the carcass location from the little sagebrush mean of 16.5 cm to the big sagebrush mean of 36.0 cm reduced detection probability by approximately 30%. Avian fence collision surveys in sagebrush-steppe habitats should be conducted at ≤2-week sampling intervals to reduce the impact of survival bias on collision rate estimates. Two-week sampling intervals may be too long in areas with low carcass and sign survival, therefore survival rates should be estimated on all study areas to determine the appropriate sampling interval duration. Researchers should be aware of the effects of local vegetation on detection probabilities, and methods to correct detection probabilities based on collision site attributes should be applied to ensure more accurate collision rate estimates. Additionally, caution should be used when aggregating or comparing uncorrected collision data from areas with differing vegetation, as detection probabilities are likely different between sites. © 2011 The Wildlife Society.     

生物物种资源监测原则与指标及抽样设计方法

生物物种资源监测是了解生物物种资源现状、开展生物物种资源保护与管理的基础工作和重要手段.阐述了生物物种资源监测的科学性原则、可操作性原则和持续性原则.提出了监测计划的制定程序;监测计划应充分考虑所具有的人力、资金和后勤保障等条件,并进行定期评估.分析了指示物种在物种资源监测中的作用与不足;认为应选择具有不同生态需求和生活史的生物类群作为监测对象.讨论了监测指标的选取方法;监测指标应可测量、有科学基础、易被公众接受、低成本和高效益;监测方法应具有科学性,能检测到相应的变化,应采用高效率、低成本的标准化监测方法.分析了现有监测计划在抽样设计方面存在的问题,探讨了空间变异性和可检测率对监测数据误差的影响及其处理方式,讨论了样本量确定和监测样地的大小、形状及位置设计.监测样地要有较好的代表性,能在有限的监测面积中较好地反映监测区域内群落种类组成与数量特征.最后,讨论了生物物种资源监测的尺度和标准化问题.     

At-line raman spectroscopy and design of experiments for robust monitoring and control of miniature bioreactor cultures

The biopharmaceutical industry is moving toward a more quality by design (QbD) approach that seeks to increase product and process understanding and process control. Miniature bioreactor systems offer a high-throughput method enabling the assessment of numerous process variables in a controlled environment. However, the number of off/at-line samples that can be taken is restricted due to the small working volume of each vessel. This limitation may be resolved through the use of Raman spectroscopy due to its ability to obtain multianalyte data from small sample volumes fast. It can, however, be challenging to implement this technique for this application due to the complexity of the sample matrix and that analytes are often present in low concentration. Here, we present a design of experiments (DOE) approach to generate samples for calibrating robust multivariate predictive models measuring glucose, lactate, ammonium, viable cell concentration (VCC) and product concentration, for unclarified cell culture that improves the daily monitoring of each miniature bioreactor vessel. Furthermore, we demonstrate how the output of the glucose and VCC models can be used to control the glucose and main nutrient feed rate within miniature bioreactor cultures to within qualified critical limits set for larger scale vessels. The DOE approach used to generate the calibration sample set is shown to result in models more robust to process changes than by simply using samples taken from the “typical” process. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2740, 2019.     

Grey Crowned Crane threat assessment around the wetlands of eastern Uganda

The Grey Crowned Crane Balearica regulorum was uplisted to globally Endangered in 2012. There is urgent need to determine the threats they face at local levels for their conservation. I conducted formal point-count surveys around the wetlands of eastern Uganda and observed no cranes. Groups of cranes were encountered when following directions from local inhabitants. Results from a questionnaire survey suggested that cranes make use of most of the wetlands in the region, and that the birds occur in small flocks averaging 6–8 individuals. Wetland loss through farming was reported to be the main threat, but I also documented poisoning, roost disturbance, and the collection of eggs and chicks. Rigorous conservation and education programs are needed to ensure the long-term survival of cranes and storks in this region.     

Fine‐scale ecological and anthropogenic variables predict the habitat use and detectability of sloth bears in the Churia habitat of east Nepal

Once widespread throughout the tropical forests of the Indian Subcontinent, the sloth bears have suffered a rapid range collapse and local extirpations in the recent decades. A significant portion of their current distribution range is situated outside of the protected areas (PAs). These unprotected sloth bear populations are under tremendous human pressures, but little is known about the patterns and determinants of their occurrence in most of these regions. The situation is more prevalent in Nepal where virtually no systematic information is available for sloth bears living outside of the PAs. We undertook a spatially replicated sign survey‐based single‐season occupancy study intending to overcome this information gap for the sloth bear populations residing in the Trijuga forest of southeast Nepal. Sloth bear sign detection histories and field‐based covariates data were collected between 2 October and 3 December 2020 at the 74 randomly chosen 4‐km² grid cells. From our results, the model‐averaged site use probability (ψ ± SE) was estimated to be 0.432 ± 0.039, which is a 13% increase from the naïve estimate (0.297) not accounting for imperfect detections of sloth bear signs. The presence of termite mound and the distance to the nearest water source were the most important variables affecting the habitat use probability of sloth bears. The average site‐level detectability (p ± SE) of sloth bear signs was estimated to be 0.195 ± 0.003 and was significantly determined by the index of human disturbances. We recommend considering the importance of fine‐scale ecological and anthropogenic factors in predicting the sloth bear‐habitat relationships across their range in the Churia habitat of Nepal, and more specifically in the unprotected areas.     

Constrained least-squares optimization for robust estimation of center of rotation

This paper presents a new direct method for estimating the average center of rotation (CoR). An existing least-squares (LS) solution has been shown by previous works to have reduced accuracy for data with small range of motion (RoM). Alternative methods proposed to improve the CoR estimation use iterative algorithms. However, in this paper we show that with a carefully chosen normalization scheme, constrained least-squares solutions can perform as well as iterative approaches, even for challenging problems with significant noise and small RoM. In particular, enforcing the normalization constraint avoids poor fits near plane singularities that can affect the existing LS method. Our formulation has an exact solution, accounts for multiple markers simultaneously, and does not depend on manually-adjusted parameters. Simulation tests compare the method to four published CoR estimation techniques. The results show that the new approach has the accuracy of the iterative methods as well as the short computation time and repeatability of a least-squares solution. In addition, application of the new method to experimental motion capture data of the thumb carpometacarpal (CMC) joint yielded a more plausible CoR location compared to the previously reported LS solution and required less time than all four alternative techniques.     

A robust methodology for kinetic model parameter estimation for biocatalytic reactions

Effective estimation of parameters in biocatalytic reaction kinetic expressions are very important when building process models to enable evaluation of process technology options and alternative biocatalysts. The kinetic models used to describe enzyme‐catalyzed reactions generally include several parameters, which are strongly correlated with each other. State‐of‐the‐art methodologies such as nonlinear regression (using progress curves) or graphical analysis (using initial rate data, for example, the Lineweaver‐Burke plot, Hanes plot or Dixon plot) often incorporate errors in the estimates and rarely lead to globally optimized parameter values. In this article, a robust methodology to estimate parameters for biocatalytic reaction kinetic expressions is proposed. The methodology determines the parameters in a systematic manner by exploiting the best features of several of the current approaches. The parameter estimation problem is decomposed into five hierarchical steps, where the solution of each of the steps becomes the input for the subsequent step to achieve the final model with the corresponding regressed parameters. The model is further used for validating its performance and determining the correlation of the parameters. The final model with the fitted parameters is able to describe both initial rate and dynamic experiments. Application of the methodology is illustrated with a case study using the ω‐transaminase catalyzed synthesis of 1‐phenylethylamine from acetophenone and 2‐propylamine. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Effects of various generations of iterative CT reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: A quantitative task-based phantom study

PurposeTo investigate how various generations of iterative reconstruction (IR) algorithms impact low-contrast detectability (LCD) in abdominal computed tomography (CT) for different patient effective diameters, using a quantitative task-based approach.MethodsInvestigations were performed using an anthropomorphic abdominal phantom with two optional additional rings to simulate varying patient effective diameters (25, 30, and 35 cm), and containing multiple spherical targets (5, 6, and 8 mm in diameter) with a 20-HU contrast difference. The phantom was scanned using routine abdominal protocols (CTDI_vol, 5.9–16 mGy) on four CT systems from two manufacturers. Images were reconstructed using both filtered back-projection (FBP) and various IR algorithms: ASiR 50%, SAFIRE 3 (both statistical IRs), ASiR-V 50%, ADMIRE 3 (both partial model-based IRs), or Veo (full model-based IR). Section thickness/interval was 2/1 mm or 2.5/1.25 mm, except 0.625/0.625 mm for Veo. We assessed LCD using a channelized Hotelling observer with 10 dense differences of Gaussian channels, with the area under the receiver operating characteristic curve (AUC) as a figure of merit.ResultsFor the smallest phantom (25-cm diameter) and smallest lesion size (5-mm diameter), AUC for FBP and the various IR algorithms did not significantly differ for any of the tested CT systems. For the largest phantom (35-cm diameter), Veo yielded the highest AUC improvement (8.5%). Statistical and partial model-based IR algorithms did not significantly improve LCD.ConclusionIn abdominal CT, switching from FBP to IR algorithms offers limited possibilities for achieving significant dose reductions while ensuring a constant objective LCD.     

Towards a robust baseline for long-term monitoring of Antarctic coastal benthos

Hydrobiologia - The Southern Ocean represents one of the world regions most sensitive to warming and there is an urgent need for quantitative data to understand changes in coastal communities. This...     

Asymptotically robust variance estimation for person‐time incidence rates

Person‐time incidence rates are frequently used in medical research. However, standard estimation theory for this measure of event occurrence is based on the assumption of independent and identically distributed (iid) exponential event times, which implies that the hazard function remains constant over time. Under this assumption and assuming independent censoring, observed person‐time incidence rate is the maximum‐likelihood estimator of the constant hazard, and asymptotic variance of the log rate can be estimated consistently by the inverse of the number of events. However, in many practical applications, the assumption of constant hazard is not very plausible. In the present paper, an average rate parameter is defined as the ratio of expected event count to the expected total time at risk. This rate parameter is equal to the hazard function under constant hazard. For inference about the average rate parameter, an asymptotically robust variance estimator of the log rate is proposed. Given some very general conditions, the robust variance estimator is consistent under arbitrary iid event times, and is also consistent or asymptotically conservative when event times are independent but nonidentically distributed. In contrast, the standard maximum‐likelihood estimator may become anticonservative under nonconstant hazard, producing confidence intervals with less‐than‐nominal asymptotic coverage. These results are derived analytically and illustrated with simulations. The two estimators are also compared in five datasets from oncology studies.     

Hierarchical multi-scale occupancy estimation for monitoring wildlife populations

Occupancy estimation is an effective analytic framework, but requires repeated surveys of a sample unit to estimate the probability of detection. Detection rates can be estimated from spatially replicated rather than temporally replicated surveys, but this may violate the closure assumption and result in biased estimates of occupancy. We present a new application of a multi-scale occupancy model that permits the simultaneous use of presence–absence data collected at 2 spatial scales and uses a removal design to estimate the probability of detection. Occupancy at the small scale corresponds to local territory occupancy, whereas occupancy at the large scale corresponds to regional occupancy of the sample units. Small-scale occupancy also corresponds to a spatial availability or coverage parameter where a species may be unavailable for sampling at a fraction of the survey stations. We applied the multi-scale occupancy model to a hierarchical sample design for 2 bird species in the Black Hills National Forest: brown creeper (Certhia americana) and lark sparrow (Chondestes grammacus). Our application of the multi-scale occupancy model is particularly well suited for hierarchical sample designs, such as spatially replicated survey stations within sample units that are typical of avian monitoring programs. The model appropriately accounts for the non-independence of the spatially replicated survey stations, addresses the closure assumption for the spatially replicated survey stations, and is useful for decomposing the observation process into detection and availability parameters. This analytic approach is likely to be useful for monitoring at local and regional scales, modeling multi-scale habitat relationships, and estimating population state variables for rare species of conservation concern. © 2011 The Wildlife Society.