Tradeoffs of different types of species occurrence data for use in systematic conservation planning

Data on the occurrence of species are widely used to inform the design of reserve networks. These data contain commission errors (when a species is mistakenly thought to be present) and omission errors (when a species is mistakenly thought to be absent), and the rates of the two types of error are inversely related. Point locality data can minimize commission errors, but those obtained from museum collections are generally sparse, suffer from substantial spatial bias and contain large omission errors. Geographic ranges generate large commission errors because they assume homogenous species distributions. Predicted distribution data make explicit inferences on species occurrence and their commission and omission errors depend on model structure, on the omission of variables that determine species distribution and on data resolution. Omission errors lead to identifying networks of areas for conservation action that are smaller than required and centred on known species occurrences, thus affecting the comprehensiveness, representativeness and efficiency of selected areas. Commission errors lead to selecting areas not relevant to conservation, thus affecting the representativeness and adequacy of reserve networks. Conservation plans should include an estimation of commission and omission errors in underlying species data and explicitly use this information to influence conservation planning outcomes.     

Experimental study of uncertainties on the macrophyte index (IBMR) based on species identification and cover

The European Water Framework Directive has required chemical and biological assessments in waterbodies. While studies of water chemistry uncertainties have existed for a long time, few studies have been carried out in hydrobiology. Our aim was to study the role of uncertainties defined as any action that may cause a data error on the French index “Indice Biologique des Macrophytes de Rivières” IBMR based on the macrophyte compartment. IBMR gives the trophic status of the river. The selected uncertainties were based on the surveyor effect both in situ and in laboratory, such as taxa omission, species identification error and cover class error. We proposed an innovative approach close to sensitivity analysis using controlled virtual changes in taxa identification and cover classes based on two confusion matrices. The creation of new experimental floristic lists and the calculation of metrics according to random specified errors allowed us to measure the effect of these errors on the IBMR and the trophic status. The taxa identification errors and combined errors (taxa identification and cover class) always had a stronger impact than cover class errors. To limit their impact, surveyor training, confrontation between surveyors and a quality control approach could be applied.     

Fix Success and Accuracy of Global Positioning System Collars in Old-Growth Temperate Coniferous Forests

Abstract: Global Positioning System (GPS) telemetry is used extensively to study animal distribution and resource selection patterns but is susceptible to biases resulting from data omission and spatial inaccuracies. These data errors may cause misinterpretation of wildlife habitat selection or spatial use patterns. We used both stationary test collars and collared free-ranging American black bears (Ursus americanus) to quantify systemic data loss and location error of GPS telemetry in mountainous, old-growth temperate forests of Olympic National Park, Washington, USA. We developed predictive models of environmental factors that influence the probability of obtaining GPS locations and evaluated the ability of weighting factors derived from these models to mitigate data omission biases from collared bears. We also examined the effects of microhabitat on collar fix success rate and examined collar accuracy as related to elevation changes between successive fixes. The probability of collars successfully obtaining location fixes was positively associated with elevation and unobstructed satellite view and was negatively affected by the interaction of overstory canopy and satellite view. Test collars were 33% more successful at acquiring fixes than those on bears. Fix success rates of collared bears varied seasonally and diurnally. Application of weighting factors to individual collared bear fixes recouped only 6% of lost data and failed to reduce seasonal or diurnal variation in fix success, suggesting that variables not included in our model contributed to data loss. Test collars placed to mimic bear bedding sites received 16% fewer fixes than randomly placed collars, indicating that microhabitat selection may contribute to data loss for wildlife equipped with GPS collars. Horizontal collar errors of >800 m occurred when elevation changes between successive fixes were >400 m. We conclude that significant limitations remain in accounting for data loss and error inherent in using GPS telemetry in coniferous forest ecosystems and that, at present, resource selection patterns of large mammals derived from GPS telemetry should be interpreted cautiously.     

Landscape as a model: the importance of geometry

In all models, but especially in those used to predict uncertain processes (e.g., climate change and nonnative species establishment), it is important to identify and remove any sources of bias that may confound results. This is critical in models designed to help support decisionmaking. The geometry used to represent virtual landscapes in spatially explicit models is a potential source of bias. The majority of spatial models use regular square geometry, although regular hexagonal landscapes have also been used. However, there are other ways in which space can be represented in spatially explicit models. For the first time, we explicitly compare the range of alternative geometries available to the modeller, and present a mechanism by which uncertainty in the representation of landscapes can be incorporated. We test how geometry can affect cell-to-cell movement across homogeneous virtual landscapes and compare regular geometries with a suite of irregular mosaics. We show that regular geometries have the potential to systematically bias the direction and distance of movement, whereas even individual instances of landscapes with irregular geometry do not. We also examine how geometry can affect the gross representation of real-world landscapes, and again show that individual instances of regular geometries will always create qualitative and quantitative errors. These can be reduced by the use of multiple randomized instances, though this still creates scale-dependent biases. In contrast, virtual landscapes formed using irregular geometries can represent complex real-world landscapes without error. We found that the potential for bias caused by regular geometries can be effectively eliminated by subdividing virtual landscapes using irregular geometry. The use of irregular geometry appears to offer spatial modellers other potential advantages, which are as yet underdeveloped. We recommend their use in all spatially explicit models, but especially for predictive models that are used in decisionmaking.     

Validating the historical record: a relative distance test and correction formula for selection bias in presettlement land surveys

Presettlement land surveys have been used throughout North America to reconstruct forest characteristics prior to major Euro‐American settlement. The bearing tree record derived from these surveys is an example of a distance‐based ecological inventory lacking strict selection rules. Such inventories pose a problem of potential selection bias if the nearest individuals are not always selected. The possibility of bias presents a major impediment to compositional analysis from bearing tree proportions. This article presents a modification to distance‐based tests and correction formulas that utilize the corner‐to‐tree distances recorded in the General Land Office (GLO) and similar surveys. The proposed modification consists of replacing absolute with relative corner‐to‐tree distances to remove the effects of density variation. Monte Carlo simulation is conducted to assess the validity, power and effectiveness of the modified test and correction formula. The modified test is found to be robust in most forests that vary in density and aggregation pattern, but exhibits some statistical bias when density and composition vary simultaneously at local scales. The correction formulas accurately reflect the direction of surveyor bias, and adjusted estimates are consistently closer to the true values than unadjusted estimates. Based on a range of simulation results, upper bound limits on the effects of selection bias can be estimated. Application to the GLO bearing tree records for the state of Minnesota indicates that Minnesota surveyors favored Pinus resinosa, P. strobus and Quercus spp. and avoided five taxa including Salix spp. and Alnus spp. The magnitude of bias appears to be small, however, with an estimated upper bound of 5–6% dissimilarity between biased and unbiased bearing tree selection, some of which may be explained by size differences among taxa.     

Observer Bias in Anuran Call Surveys

ABSTRACT Amphibian monitoring programs rarely question the quality of data obtained by observers and often ignore observer bias. In order to test for bias in amphibian call surveys, we sampled 29 clusters of wetlands from the Rainwater Basin, Nebraska, USA, totaling 228 functionally connected wetlands. Sampling consisted of 3-minute stops where volunteers recorded species heard and made digital recordings. Based on 627 samples, we examined 3 types of observer bias: omission, false inclusion (commission), and incorrect identification. Misidentification rates ranged from 4.2% to 18.3%. Relatively high and unquantified error rates can negatively affect the ability of monitoring programs to accurately detect the population or abundance trends for which most were designed.     

洞庭湖洲滩速生杨树林变化信息提取方法

洞庭湖是我国第二大淡水湖,其湿地资源具有重要的生态功能和经济价值。近20年来,洞庭湖洲滩速生杨树林发展迅速,其中西洞庭湖杨树林的扩张最为明显,极大改变了湖区湿地植被分布格局,隐含极大的生态风险。以Landsat ETM+和HJ-1A/1B CCD影像为数据源,提出了洞庭湖速生杨树林变化信息提取的两种方法,并对这两种方法进行了比较研究。一种是分类的方法,即采用面向对象分层信息提取的方法先提取出树林滩地信息,再将距离大堤一定范围内的树林滩地归为防护林,速生杨树林变化的面积即为两个时相提取结果的差值。另一种是变化检测的方法,它是基于像元进行变化检测,先确定出总的变化区域,再从中筛选速生杨树林的变化信息。结果表明:(1)两种提取方法都是可行的,不同方法提取的速生林变化信息存在一定差异,但空间分布大体一致;(2)基于分类的方法总体精度和Kappa系数均略高于基于变化检测的方法:其中基于分类的方法总体精度达84.00%,Kappa系数为0.67,基于变化检测的方法总体精度达83.00%,Kappa系数为0.65;(3)基于分类的方法图斑较大、图斑数较少,基于变化检测的方法图斑较小且较破碎、图斑数多;(4)基于分类的方法漏分较少、错分较多,基于变化检测的方法漏分较多、错分较少。为洞庭湖洲滩杨树林的动态监测提供了研究方法,也为杨树林扩张原因及其生态效应分析提供研究基础。     

Installing chainsaw‐carved hollows in medium‐sized live trees increases rates of visitation by hollow‐dependent fauna

Anthropogenic disturbance has resulted in a global reduction in the abundance of mature, hollow‐bearing trees. Nest boxes have long been used to provide supplementary shelter sites in revegetated and regenerating landscapes, but limitations in their effectiveness when offsetting the loss of mature trees has led to increased interest in novel designs of artificial hollows. For example, mechanically excavating cavities into the trunk or branches of trees. However, the effectiveness of artificial hollows in attracting wildlife to visit small‐ or medium‐sized, growing trees in human‐disturbed landscapes has received little attention. In this study, we installed chainsaw hollows that were designed for small, hollow‐dependent mammals and birds into the trunks of live medium‐sized trees. We conducted a before‐after control‐impact experiment using passive camera traps to monitor changes in visitations by wildlife to (1) mature hollow‐bearing trees, (2) developing trees without hollows (i.e. control trees), and (3) developing trees with newly installed chainsaw hollows. We found that, compared to large hollow‐bearing trees and control trees, the developing trees that were selected for chainsaw hollow construction showed the greatest visitation rates by hollow‐dependent wildlife (i.e. number of visits) during the “post‐impact” surveys. Our results suggest that chainsaw hollows designed to replicate the external physical characteristics of natural tree hollows could be effective in attracting target hollow‐dependent fauna to developing trees in regenerating and revegetated landscapes. Further studies are required to compare the effectiveness of natural hollows, chainsaw hollows, and nest boxes when deployed in a range of human‐disturbed landscapes.     

Geographic sampling bias in the South African Frog Atlas Project: implications for conservation planning

Quality conservation planning requires quality input data. However, the broad scale sampling strategies typically employed to obtain primary species distribution data are prone to geographic bias in the form of errors of omission. This study provides a quantitative measure of sampling bias to inform accuracy assessment of conservation plans based on the South African Frog Atlas Project. Significantly higher sampling intensity near to cities and roads is likely to result in overstated conservation priority and heightened conservation conflicts in urban areas. Particularly well sampled protected areas will also erroneously appear to contribute highly to amphibian biodiversity targets. Conversely, targeted sampling in the arid northwest and along mountain ranges is needed to ensure that these under-sampled regions are not excluded from conservation plans. The South African Frog Atlas Project offers a reasonably accurate picture of the broad scale west-to-east increase in amphibian richness and abundance, but geographic bias may limit its applicability for fine scale conservation planning. The Global Amphibian Assessment species distribution data offered a less biased alternative, but only at the cost of inflated commission error.     

Effect of detection heterogeneity in occupancy‐detection models: an experimental test of time‐to‐first‐detection methods

Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time‐to‐first‐detection (TTD) occupancy models have been proposed as a cost–effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy‐detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala Phascolarctos cinereus populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness‐of‐fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy‐detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness‐of‐fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy‐detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.     

Technical bias and the reproducibility crisis: The problem of systemic errors resulting from artefacts of equipment,methods or dataset has been underappreciated

Technical bias can cause lack of reproducibility. While harder to identify than other bias, it can cause consistent systemic errors in experimental data and analysis. Subject Categories: Methods & Resources, S&S: Ethics

The problem of reproducibility of experimental results has been festering for some years and prompted a great deal of research and analysis to explain why so many studies in biomedical research cannot be validated. Among these, the role played by technical bias in yielding erroneous results has been largely underappreciated in contrast to better‐known sources of error, such as inadequate sample sizes, selective reporting or publication pressure. But its significance has come to the fore after recent studies raised questions over earlier findings that were shown to have been warped by technical bias.

Technical bias often occurs alongside other forms of bias and the effects can be additive as well as overlapping.

Technical bias has nothing directly to do with human psychology or deficient analysis, but results from artefacts of equipment, reagents and laboratory methods, as well as lack of standard protocols. As a result, it has contributed to the reproducibility crisis both between (inter) and within (intra) institutions or even individual laboratories. Technical bias often occurs alongside other forms of bias and the effects can be additive as well as overlapping. Some of these apply more to particular fields than others, with sociological research tending to be plagued by different biases than say physics or chemistry. Overall, one could identify six broad categories alongside technical bias.The one most closely related to technical bias is statistical bias, sometimes called sampling error, which arises when the data are not representative of the wider set from which the results are expected. Either the sampling size for an experiment or survey is too small or it is skewed towards a subsector of the whole. The latter can often distort surveys purely because people who agree to take part often differ in some ways from those who decline to do so.Then, there is measurement bias caused by systematic errors in the process of collecting or generating data. A simple example would be measuring height without asking people to take off their shoes. The error would vary between individuals but the apparent average and mean would be too high.Third is confirmation bias, often defined as an unconscious tendency to seek out, recall or notice data that confirms preconceived opinions or hypotheses. This has been cited in the context of the COVID‐19 pandemic where aspects such as hospitalization levels or mutation rates are taken to confirm worst fears when they may be just normal artefacts of a respiratory virus.Fourth is design bias, where misleading results can arise out of the way an experiment or investigation is constructed. This has overlap with some of the other biases since design involves selection of samples, generation of data and the methods used.Fifth is publication bias: the tendency to pursue research and to report results that have a positive outcome, reinforced by journals'' preference for such papers. This is closely related to positive outcome bias and can lead to an overall misleading and rosy picture of research in a field. Egregious examples include the infamous Andrew Wakefield paper falsely connecting the MMR vaccine with autism, which may well not have been published without this tendency towards publicity‐grabbing headlines.Finally, there is hindsight bias, a common human affliction that is the tendency to regard events as having been predictable only after they have occurred. This can lead researchers to subconscious revisionism, for example raising the average prior probability of a given outcome after that has come to pass.

… there is hindsight bias, a common human affliction that is the tendency to regard events as having been predictable only after they have occurred.

     

Is cross-validation valid for small-sample microarray classification?

MOTIVATION: Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable understanding of the behavior of cross-validation in the context of very small samples. RESULTS: An extensive simulation study has been performed comparing cross-validation, resubstitution and bootstrap estimation for three popular classification rules-linear discriminant analysis, 3-nearest-neighbor and decision trees (CART)-using both synthetic and real breast-cancer patient data. Comparison is via the distribution of differences between the estimated and true errors. Various statistics for the deviation distribution have been computed: mean (for estimator bias), variance (for estimator precision), root-mean square error (for composition of bias and variance) and quartile ranges, including outlier behavior. In general, while cross-validation error estimation is much less biased than resubstitution, it displays excessive variance, which makes individual estimates unreliable for small samples. Bootstrap methods provide improved performance relative to variance, but at a high computational cost and often with increased bias (albeit, much less than with resubstitution).     

An approximately unbiased test of phylogenetic tree selection

An approximately unbiased (AU) test that uses a newly devised multiscale bootstrap technique was developed for general hypothesis testing of regions in an attempt to reduce test bias. It was applied to maximum-likelihood tree selection for obtaining the confidence set of trees. The AU test is based on the theory of Efron et al. (Proc. Natl. Acad. Sci. USA 93:13429-13434; 1996), but the new method provides higher-order accuracy yet simpler implementation. The AU test, like the Shimodaira-Hasegawa (SH) test, adjusts the selection bias overlooked in the standard use of the bootstrap probability and Kishino-Hasegawa tests. The selection bias comes from comparing many trees at the same time and often leads to overconfidence in the wrong trees. The SH test, though safe to use, may exhibit another type of bias such that it appears conservative. Here I show that the AU test is less biased than other methods in typical cases of tree selection. These points are illustrated in a simulation study as well as in the analysis of mammalian mitochondrial protein sequences. The theoretical argument provides a simple formula that covers the bootstrap probability test, the Kishino-Hasegawa test, the AU test, and the Zharkikh-Li test. A practical suggestion is provided as to which test should be used under particular circumstances.     

Influences of transect relocation errors on line-point estimates of plant cover

The line-point transect method has been used to estimate plant cover for about nine decades. In particular, the method is often used to determine baseline plant cover and monitor for changes in plant cover over time. In such cases, detection of change requires both the initial transect starting position and angle of orientation are exact in relocation without error. A study was conducted on influences of errors in basal cover estimates that resulted from inexact relocation and orientation of a resample transect. Simulation studies of actual field data showed that variation in plant cover estimates from relocated line-point transects increased with each source of error and combinations of these errors. Relocated transects resulted in unbiased estimates of total-plant cover only when means over all transects are used to detect changes over time. Substantial errors were observed when the mean cover of individually relocated transect was compared to its original transect.     

Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data

Global positioning system (GPS) technologies collect unprecedented volumes of animal location data, providing ever greater insight into animal behaviour. Despite a certain degree of inherent imprecision and bias in GPS locations, little synthesis regarding the predominant causes of these errors, their implications for ecological analysis or solutions exists. Terrestrial deployments report 37 per cent or less non-random data loss and location precision 30 m or less on average, with canopy closure having the predominant effect, and animal behaviour interacting with local habitat conditions to affect errors in unpredictable ways. Home-range estimates appear generally robust to contemporary levels of location imprecision and bias, whereas movement paths and inferences of habitat selection may readily become misleading. There is a critical need for greater understanding of the additive or compounding effects of location imprecision, fix-rate bias, and, in the case of resource selection, map error on ecological insights. Technological advances will help, but at present analysts have a suite of ad hoc statistical corrections and modelling approaches available—tools that vary greatly in analytical complexity and utility. The success of these solutions depends critically on understanding the error-inducing mechanisms, and the biggest gap in our current understanding involves species-specific behavioural effects on GPS performance.     

A model-based analysis of microarray experimental error and normalisation

A statistical model is proposed for the analysis of errors in microarray experiments and is employed in the analysis and development of a combined normalisation regime. Through analysis of the model and two-dye microarray data sets, this study found the following. The systematic error introduced by microarray experiments mainly involves spot intensity-dependent, feature-specific and spot position-dependent contributions. It is difficult to remove all these errors effectively without a suitable combined normalisation operation. Adaptive normalisation using a suitable regression technique is more effective in removing spot intensity-related dye bias than self-normalisation, while regional normalisation (block normalisation) is an effective way to correct spot position-dependent errors. However, dye-flip replicates are necessary to remove feature-specific errors, and also allow the analyst to identify the experimentally introduced dye bias contained in non-self-self data sets. In this case, the bias present in the data sets may include both experimentally introduced dye bias and the biological difference between two samples. Self-normalisation is capable of removing dye bias without identifying the nature of that bias. The performance of adaptive normalisation, on the other hand, depends on its ability to correctly identify the dye bias. If adaptive normalisation is combined with an effective dye bias identification method then there is no systematic difference between the outcomes of the two methods.     

Effects of survey methods on burrowing owl behaviors

Monitoring wildlife populations often involves intensive survey efforts to attain reliable estimates of population size. Such efforts can increase disturbance to animals, alter detection, and bias population estimates. Burrowing owls (Athene cunicularia) are declining across western North America, and information on the relative effects of potential survey methods on owl behaviors is needed. We designed a field experiment to compare burrowing owl flight distances, times displaced, and probabilities of being displaced between 4 potential population survey methods (single walking surveyor, single vehicle stop, single vehicle stop with 2 surveyors, and double vehicle stop with 2 surveyors), and an experimental control in the agricultural matrix of Imperial Valley, California. Between 25 April and 1 May 2008, we randomly applied survey methods to 395 adult male owls during daylight hours (0700 hours through 1900 hours). All survey methods increased odds of displacing owls from perches. Survey methods with observers outside the vehicle were 3 times more likely to displace an owl than a single vehicle stop where observers remained inside the vehicle. Owls were displaced farther distances by all survey methods compared to control trials, but distances and time displaced did not differ among survey methods. We recommend that surveys for counting owls during the breeding season in agroecystems like the Imperial Valley where high densities of owls nest primarily along the borders of fields be conducted using single vehicle stops with or without 2 surveyors, depending on conditions for locating owls from roads. © 2011 The Wildlife Society.     

Estimation and Correction of Visibility Bias in Aerial Surveys of Wintering Ducks

Abstract: Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1–100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36–42%, and associated standard errors increased 38–55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):808–813; 2008)