Spatial detection of outlier loci with Moran eigenvector maps

The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two‐step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two‐step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow‐up with MSR generally reduced (already low) false‐positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual‐based and population‐based sampling by leveraging spatial information that has hitherto been neglected.     

Testing and interpreting the shared space‐environment fraction in variation partitioning analyses of ecological data

Variation partitioning analyses combined with spatial predictors (Moran's eigenvector maps, MEM) are commonly used in ecology to test the fractions of species abundance variation purely explained by environment and space. However, while these pure fractions can be tested using a classical residuals permutation procedure, no specific method has been developed to test the shared space‐environment fraction (SSEF). Yet, the SSEF is expected to encompass a major driver of community assembly, that is, an induced spatial dependence effect (ISD; i.e. the reflection of a spatially structured habitat filter on a species distribution). A reliable test of this fraction is therefore crucial to properly test the presence of an ISD on ecological data. To bridge the gap, we propose to test the SSEF through spatially‐constrained null models: torus‐translations, and Moran spectral randomisations. We investigated the type I error rate and statistical power of our method based on two real environmental datasets and simulations of tree distributions. Ten types of tree distribution displaying contrasted aggregation properties were simulated, and their abundances were sampled in 153 regularly‐distributed 20 × 20 m quadrats. The SSEF was tested for 1000 simulated tree distributions either unrelated to the environment, or filtered by environmental variables displaying contrasting spatial structures. The method proposed provided a correct type I error rate (< 0.05). The statistical power was high (> 0.9) when abundances were filtered by an environmental variable structured at broad scale. However, the spatial resolution allowed by the sampling design limited the power of the method when using a fine‐scale filtering variable. This highlighted that an ISD can be properly detected providing that the spatial pattern of the filtering process is correctly captured by the sampling design of the study. An R function to apply the SSEF testing method is provided and detailed in a tutorial.     

Nonparametric One‐way Analysis of Variance of Replicated Bivariate Spatial Point Patterns

A common problem in neuropathological studies is to assess the spatial patterning of cells on tissue sections and to compare spatial patterning between disorder groups. For a single cell type, the cell positions constitute a univariate point process and interest focuses on the degree of spatial aggregation. For two different cell types, the cell positions constitute a bivariate point process and the degree of spatial interaction between the cell types is of interest. We discuss the problem of analysing univariate and bivariate spatial point patterns in the one‐way design where cell patterns have been obtained for groups of subjects. A bootstrapping procedure to perform a nonparametric one‐way analysis of variance of the spatial aggregation of a univariate point process has been suggested by Diggle, Lange and Bene? (1991). We extend their replication‐based approach to allow the comparison of the spatial interaction of two cell types between groups, to include planned comparisons (contrasts) and to assess whole groups against complete spatial randomness and spatial independence. We also accommodate several replicate tissue sections per subject. An advantage of our approach is that it can be applied when processes are not stationary, a common problem in brain tissue sections since neurons are arranged in cortical layers. We illustrate our methods by applying them to a neuropathological study to investigate abnormalities in the functional relationship between neurons and astrocytes in HIV associated dementia. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Maximizing plant species inventory efficiency by means of remotely sensed spectral distances

Aim Inventorying plant species in an area based on randomly placed quadrats can be quite inefficient. The aim of this paper is to test whether plant species richness can be inventoried more efficiently by means of a spectrally‐based ordering of sites to be sampled. Location The study area was a complex wetland ecosystem, the Lake Montepulciano Nature Reserve, central Italy. This is one of the most important wetland areas of central Italy because of the diverse plant communities and the seasonal avifauna. Methods Field sampling, based on a random stratified sampling design, was performed in June 2002. Plant species composition was recorded within sampling units of 100 m² (plots) and 1 ha (macroplots). A QuickBird multispectral image of the same date was acquired and corrected both geometrically and radiometrically. Species accumulation curves based on spectral information were obtained by ordering sites to be sampled according to a maximum spectral distance criterion (i.e. by ordering sampling units based on the maximum distances among them in a four‐dimensional spectral space derived from the remotely sensed data). Different distance measures based on mean and maximum spectral distances among sampling units were tested. The performance of the species accumulation curve derived by the spectrally‐based ordering of sampling units was tested against a rarefaction curve obtained from the mean of 10,000 accumulation curves based on randomly ordered sampling units. Results The spectrally‐derived curve based on the maximum spectral distance among sampling units showed the most rapid accumulation of species, well above the rarefaction curve, at both the plot and the macroplot scales. Other ordering criteria of sampling units captured less richness over most of the species accumulation curves at both the spatial scales. The accumulation curves based on other measurements of distance were much closer to the random curve and did not show differences with respect to the species rarefaction curve based on random ordering of sampling units. Main conclusions The present investigation demonstrated that spectral‐based ordering of sites to be sampled can lead to the maximization of the efficiency of plant species inventories, an activity usually driven by the ‘botanist's internal algorithm’ (intuition), without any formalized rule to drive field sampling. The proposed approach can reduce costs of plant species inventorying through a more efficient allotment of time and sampling.     

A field test of the distance sampling method using Golden‐cheeked Warblers

ABSTRACT Criteria for delisting Golden‐cheeked Warblers (Dendroica chrysoparia) include protection of sufficient breeding habitat to ensure the continued existence of 1000 to 3000 singing males in each of eight recovery regions for ≥10 consecutive years. Hence, accurate abundance estimation is an integral component in the recovery of this species. I conducted a field test to determine if the distance sampling method provided unbiased abundance estimates for Golden‐cheeked Warblers and develop recommendations to improve the accuracy of estimates by minimizing the effects of violating this method's assumptions. To determine if observers could satisfy the assumptions that birds are detected at the point with certainty and at their initial locations, I compared point‐transect sampling estimates from 2‐, 3‐, 4‐, and 5‐min time intervals to actual abundance determined by intensive territory monitoring. Point‐transect abundance estimates were 15%, 29%, 43%, and 59% greater than actual abundance (N= 156) for the 2‐, 3‐, 4‐, and 5‐min intervals, respectively. Point‐transect sampling produced unbiased estimates of Golden‐cheeked Warbler abundance when counts were limited to 2 min (N= 154–207). Abundance estimates derived from point‐transect sampling were likely greater than actual abundance because observers did not satisfy the assumption that birds were detected at their initial locations due to the frequent movements and large territory sizes of male Golden‐cheeked Warblers. To minimize the effect of movement on abundance estimates, I recommend limiting counts of singing males to 2‐min per point. Counts for other species in similar habitats with similar behavior and movement patterns also should be limited to 2 min when unbiased estimates are important and conducting field tests of the point‐transect distance sampling method is not possible.     

Hyperspectral scanning laser optical tomography

In order to study physical relationships within tissue volumes or even organism‐level systems, the spatial distribution of multiple fluorescent markers needs to be resolved efficiently in three dimensions. Here, rather than acquiring discrete spectral images sequentially using multiple emission filters, a hyperspectral scanning laser optical tomography system is developed to obtain hyperspectral volumetric data sets with 2‐nm spectral resolution of optically transparent mesoscopic (millimeter‐centimeter) specimens. This is achieved by acquiring a series of point‐scanning hyperspectral extended depth of field images at different angles and subsequently tomographically reconstructing the 3D intensity distribution for each wavelength. This technique is demonstrated to provide robust measurements via the comparison of spectral and intensity profiles of fluorescent bead phantoms. Due to its enhanced spectral resolving ability, this technique is also demonstrated to resolve largely overlapping fluorophores, as demonstrated by the 3D fluorescence hyperspectral reconstruction of a dual‐labeled mouse thymus gland sample and the ability to distinguish tumorous and normal tissues of an unlabeled mouse intestine sample.      

Improved species‐occurrence predictions in data‐poor regions: using large‐scale data and bias correction with down‐weighted Poisson regression and Maxent

Species distribution modelling (SDM) has become an essential method in ecology and conservation. In the absence of survey data, the majority of SDMs are calibrated with opportunistic presence‐only data, incurring substantial sampling bias. We address the challenge of correcting for sampling bias in the data‐sparse situations. We modelled the relative intensity of bat records in their entire range using three modelling algorithms under the point‐process modelling framework (GLMs with subset selection, GLMs fitted with an elastic‐net penalty, and Maxent). To correct for sampling bias, we applied model‐based bias correction by incorporating spatial information on site accessibility or sampling efforts. We evaluated the effect of bias correction on the models’ predictive performance (AUC and TSS), calculated on spatial‐block cross‐validation and a holdout data set. When evaluated with independent, but also sampling‐biased test data, correction for sampling bias led to improved predictions. The predictive performance of the three modelling algorithms was very similar. Elastic‐net models have intermediate performance, with slight advantage for GLMs on cross‐validation and Maxent on hold‐out evaluation. Model‐based bias correction is very useful in data‐sparse situations, where detailed data are not available to apply other bias correction methods. However, bias correction success depends on how well the selected bias variables describe the sources of bias. In this study, accessibility covariates described bias in our data better than the effort covariate, and their use led to larger changes in predictive performance. Objectively evaluating bias correction requires bias‐free presence–absence test data, and without them the real improvement for describing a species’ environmental niche cannot be assessed.     

A new digital method of data collection for spatial point pattern analysis in grassland communities

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community‐level patterns, and ecological processes. In this study, we developed a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub‐blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0–0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a geographical information system, to collect experimental data on the spatial point patterns for the populations in this grassland community.     

Monitoring an Arizona Ponderosa Pine Restoration: Sampling Efficiency and Multivariate Analysis of Understory Vegetation

As monitoring plans for the restoration of Pinus ponderosa forests in the southwestern United States evolve toward examining multifactor ecosystem responses to ecological restoration, designing efficient sampling procedures for understory vegetation will become increasingly important. The objective of this study was to compare understory composition and diversity among thin/burn and control treatments in a P. ponderosa restoration, while simultaneously examining the effects of sampling design and multivariate analyses on which conclusions were based. Using multi‐response permutation procedures (MRPP), we tested the null hypothesis of no difference in understory species composition among treatments using different data matrices (e.g., frequency and cover) for two different sampling methods. Treatment differences were subtle and were detected by an intensive 50, 1‐m² subplot sampling method for all data matrices but were not detected by a less intensive point‐intercept sampling method for any matrix. Sampling methods examined in this study controlled results of multivariate analyses more than the data matrices used to summarize data generated by a sampling method. We partitioned data into plant life form and native/exotic species categories for MRPP, and this partitioning isolated plant groups most responsible for treatment differences. We also examined the effects of number of 1‐m² subplots sampled on mean‐species‐richness/m² estimates and found that estimates based on 10 subplots and based on 50 subplots were highly correlated (r = 0.99). Species–area curves indicated that the 50, 1‐m² subplot sampling method detected the common species of sites but failed to detect the majority of rare species. Additional sampling‐design studies are needed to develop single sampling designs that produce multifactor data on plant composition, diversity, and spatial patterns amenable to multivariate analyses as part of monitoring plans of vegetation responses to ecological restoration.     

Near‐infrared human finger measurements based on self‐calibration point: Simulation and in vivo experiments

Near‐infrared light allows measuring tissue oxygenation. These measurements relay on oxygenation‐dependent absorption spectral changes. However, the tissue scattering, which is also spectral dependent, introduces an intrinsic error. Most methods focus on the volume reflectance from a semi‐infinite sample. We have proposed examining the full scattering profile (FSP), which is the angular intensity distribution. A point was found, that is, the iso‐path length (IPL) point, which is not dependent on the tissue scattering, and can serve for self‐calibration. This point is geometric dependent, hence in cylindrical tissues depends solely on the diameter. In this work, we examine an elliptic tissue cross section via Monte Carlo simulation. We have found that the IPL point of an elliptic tissue cross section is indifferent to the input illumination orientation. Furthermore, the IPL point is the same as in a circular cross section with a radius equal to the effective ellipse radius. This is despite the fact that the FSPs of the circular and elliptical cross sections are different. Hence, changing the orientation of the input illumination reveals the IPL point. In order to demonstrate this experimentally, the FSPs of a few female fingers were measured at 2 perpendicular orientations. The crossing point between these FSPs was found equivalent to the IPL point of a cylindrical phantom with a radius similar to the effective radius. The findings of this work will allow accurate pulse oximetry assessment of blood saturation.      

植物空间分布格局中邻体距离的概率分布模型及参数估计

最近邻体法是一类有效的植物空间分布格局分析方法,邻体距离的概率分布模型用于描述邻体距离的统计特征,属于常用的最近邻体法之一。然而,聚集分布格局中邻体距离(个体到个体)的概率分布模型表达式复杂,参数估计的计算量大。根据该模型期望和方差的特性,提出了一种简化的参数估计方法,并利用遗传算法来实现参数优化,结果表明遗传算法可以有效地估计的该模型的两个参数。同时,利用该模型拟合了加拿大南温哥华岛3个寒温带树种的空间分布数据,结果显示:该概率分布模型可以很好地拟合美国花旗松(P.menziesii)和西部铁杉(T.heterophylla)的邻体距离分布,但由于西北红柏(T.plicata)存在高度聚集的团簇分布,拟合结果不理想;美国花旗松在样地中近似随机分布,空间聚集参数对空间尺度的依赖性不强,但西北红柏和西部铁杉空间聚集参数具有尺度依赖性,随邻体距离阶数增加而变大。最后,讨论了该模型以及参数估计方法的优势和限制。     

Two‐Photon polymerization for microfabrication of three‐dimensional scaffolds for tissue engineering application

In natural tissues cells are embedded in a three‐dimensional fibrous network of biopolymers like collagen, hyaluronic acid etc. This extracellular matrix (ECM) influences the cell fate, the differentiation status, metabolic processes and provides structural integrity. For a three‐dimensional or physiological cell cultivation that are required in biomedical applications (e.g. tissue engineering, BioMEMS) scaffolds are needed. These scaffolds mimic the ECM according to their biocompatibility which comprises aspects of surface compatibility and importantly for tissue engineering applications aspects of structural compatibility. We have evaluated scaffold design parameters for the three‐dimensional cultivation of chondrocytes for the tissue engineering of artificial cartilage. Two‐photon polymerization is a powerful technique for fabrication of polymeric three‐dimensional micro‐ and submicro‐structures. The photoinitiation system for two‐photon polymerization is excited by simultaneous absorption of two photons leading to chemical polymerization reactions. Due to a tight confinement of the excitation volume around the focal point, this method can produce micrometer sized objects maintaining a high spatial resolution down to 100 nm. Two‐photon processes require very high photon densities which are provided by pulsed femtosecond lasers. The potential of this approach for microfabrication of scaffolds for tissue engineering is demonstrated by investigation of the cell response to microstructures with complex three‐dimensional geometry and feature sizes in the range of few micrometers.     

Optimizing ultrasonic sampling effort for monitoring forest bats

Landscape‐scale monitoring is a key approach for assessing changes in indicators. However, great care needs to be taken to collect rigorous data and avoid wasting resources in long‐term programmes. Insect‐eating bats are diverse, functionally important and are often proposed as indicator species of environmental health. We used acoustic (ultrasonic) data from pilot bat surveys undertaken in forests and woodlands to optimize sampling effort to produce precise estimates of bat activity and occupancy. We also carried out simulations to evaluate the statistical power of different sampling designs to detect changes in activity and occupancy levels of individual bat species. There was little gain in precision for estimates of bat activity by sampling beyond five to six detector nights. To ensure spatial heterogeneity was sampled around a monitoring point, three detectors for two nights or two detectors for three nights would be required. This level of sampling was also sufficient to be 90% certain of recording occupancy for 11 of 12 taxa. Power simulations revealed that a sampling design using two detectors per monitoring point for two nights could detect a 30% decline within 10 years with 90% power for all species, except the white‐striped free tail bat (Tadarida australis), using either changes in activity levels or occupancy. However, fewer years were required when using occupancy. Setting detectors either on‐flyways or off‐flyways contributed only minor differences to the time taken to reach 90% power for both occupancy and activity levels, though sampling both locations has major implications for interpreting trends in bats. We suggest that bat activity levels are more sensitive for detecting change than occupancy because one pass or 1000 passes can be recorded per night by an acoustic detector, and this is not differentiated by occupancy. Bats can be monitored cost‐effectively and should be included in monitoring programmes.     

Improved nonparametric estimation of the optimal diagnostic cut‐off point associated with the Youden index under different sampling schemes

A diagnostic cut‐off point of a biomarker measurement is needed for classifying a random subject to be either diseased or healthy. However, the cut‐off point is usually unknown and needs to be estimated by some optimization criteria. One important criterion is the Youden index, which has been widely adopted in practice. The Youden index, which is defined as the maximum of (sensitivity + specificity ?1), directly measures the largest total diagnostic accuracy a biomarker can achieve. Therefore, it is desirable to estimate the optimal cut‐off point associated with the Youden index. Sometimes, taking the actual measurements of a biomarker is very difficult and expensive, while ranking them without the actual measurement can be relatively easy. In such cases, ranked set sampling can give more precise estimation than simple random sampling, as ranked set samples are more likely to span the full range of the population. In this study, kernel density estimation is utilized to numerically solve for an estimate of the optimal cut‐off point. The asymptotic distributions of the kernel estimators based on two sampling schemes are derived analytically and we prove that the estimators based on ranked set sampling are relatively more efficient than that of simple random sampling and both estimators are asymptotically unbiased. Furthermore, the asymptotic confidence intervals are derived. Intensive simulations are carried out to compare the proposed method using ranked set sampling with simple random sampling, with the proposed method outperforming simple random sampling in all cases. A real data set is analyzed for illustrating the proposed method.     

Optical quantitative pathology of cervical intraepithelial neoplasia in human tissues using spatial frequency analysis

An optical quantitative histological method in human tissues using spatial frequencies is demonstrated. Optical spatial frequency spectra from different stages of human Cervical Intraepithelial Neoplasia (CIN) tissue are evaluated as a potential quantitative pathological tool. The degree of randomness of tissue structures from normal to different stages of CIN tissue can be recognized by spatial frequency analysis. The standard deviation, σ of human normal and CIN tissue, is obtained by assuming the spatial frequency spectra as a Gaussian distribution. A support vector machine classifier (SVM) is trained in the subspace of σ. Twenty‐eight normal and CIN samples of varying grades are examined and compared with current diagnostic outcomes. Our results suggest that an excellent accuracy for diagnostic purposes can be achieved. This approach offers a simple, efficient and objective way to supplement histopathology in recognizing alterations from normal to different stages of cervical pre‐cancer, which are reflected by spatial information contained within the aperiodic and random structures of the different types of tissue. (© 2015 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)     

A grid‐based method for sampling and analysing spatially ambiguous plants

Abstract. Spatial data can provide much information about the interrelations of plants and the relationship between individuals and the environment. Spatially ambiguous plants, i.e. plants without readily identifiable loci, and plants that are profusely abundant, present non‐trivial impediments to the collection and analysis of vegetation data derived from standard spatial sampling techniques. Sampling with grids of presence/absence quadrats can ameliorate much of this difficulty. Our analysis of 10 fully‐mapped grassland plots demonstrates the applicability of the grid‐based approach which revealed spatial dependence at a much lower sampling effort than mapping each plant. Ripley's K‐function, a test commonly used for point patterns, was effective for pattern analysis on the grids and the gridded quadrat technique was an effective tool for quantifying spatial patterns. The addition of spatial pattern measures should allow for better comparisons of vegetation structure between sites, instead of sole reliance on species composition data.     

Quantifying soil organic carbon in complex landscapes: an example of grassland undergoing encroachment of woody plants

The invasion of woody plants into grass‐dominated ecosystems has occurred worldwide during the past century with potentially significant impacts on soil organic carbon (SOC) storage, ecosystem carbon sequestration and global climate warming. To date, most studies of tree and shrub encroachment impacts on SOC have been conducted at small scales and results are equivocal. To quantify the effects of woody plant proliferation on SOC at broad spatial scales and to potentially resolve inconsistencies reported from studies conducted at fine spatial scales, information regarding spatial variability and uncertainty of SOC is essential. We used sequential indicator simulation (SIS) to quantify spatial uncertainty of SOC in a grassland undergoing shrub encroachment in the Southern Great Plains, USA. Results showed that both SOC pool size and its spatial uncertainty increased with the development of woody communities in grasslands. Higher uncertainty of SOC in new shrub‐dominated communities may be the result of their relatively recent development, their more complex above‐ and belowground architecture, stronger within‐community gradients, and a greater degree of faunal disturbance. Simulations of alternative sampling designs demonstrated the effects of spatial uncertainty on the accuracy of SOC estimates and enabled us to evaluate the efficiency of sampling strategies aimed at quantifying landscape‐scale SOC pools. An approach combining stratified random sampling with unequal point densities and transect sampling of landscape elements exhibiting strong internal gradients yielded the best estimates. Complete random sampling was less effective and required much higher sampling densities. Results provide novel insights into spatial uncertainty of SOC and its effects on estimates of carbon sequestration in terrestrial ecosystem and suggest effective protocol for the estimating of soil attributes in landscapes with complex vegetation patterns.     

Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models

Aim Environmental niche models that utilize presence‐only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for ‘training’ and ‘testing’ purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation. Location The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana. Methods Two types of niche modelling algorithms – the genetic algorithm for rule‐set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) – were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence‐only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. Results The accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusions The findings indicate that care should be taken when interpreting the results from presence‐only niche models when training and test data have been randomly partitioned but occurrence data were non‐randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions.     

Hyperspectral characterization of re‐epithelialization in an in vitro wound model

In vitro wound models are useful for research on wound re‐epithelialization. Hyperspectral imaging represents a non‐destructive alternative to histology analysis for detection of re‐epithelialization. This study aims to characterize the main optical behavior of a wound model in order to enable development of detection algorithms. K‐Means clustering and agglomerative analysis were used to group spatial regions based on the spectral behavior, and an inverse photon transport model was used to explain differences in optical properties. Six samples of the wound model were prepared from human tissue and followed over 22 days. Re‐epithelialization occurred at a mean rate of 0.24 mm²/day after day 8 to 10. Suppression of wound spectral features was the main feature characterizing re‐epithelialized and intact tissue. Modeling the photon transport through a diffuse layer placed on top of wound tissue properties reproduced the spectral behavior. The missing top layer represented by wounds is thus optically detectable using hyperspectral imaging.     

Geostatistical approaches and optimal additional sampling schemes for spatial patterns and future sampling of bird diversity

Aim To evaluate geostatistical approaches, namely kriging, co‐kriging and geostatistical simulation, and to develop an optimal sampling design for mapping the spatial patterns of bird diversity, estimating their spatial autocorrelations and selecting additional samples of bird diversity in a 2450 km² basin. Location Taiwan. Methods Kriging, co‐kriging and simulated annealing are applied to estimate and simulate the spatial patterns of bird diversity. In addition, kriging and co‐kriging with a genetic algorithm are used to optimally select further samples to improve the kriging and co‐kriging estimations. The association between bird diversity and elevation, and bird diversity and land cover, is analysed with estimated and simulated maps. Results The Simpson index correlates spatially with the normalized difference vegetation index (NDVI) within the micro‐scale and the macro‐scale in the study basin, but the Shannon diversity index only correlates spatially with NDVI within the micro‐scale. Co‐kriging and simulated annealing simulation accurately simulate the statistical and spatial patterns of bird diversity. The mean estimated diversity and the simulated diversity increase with elevation and decrease with increasing urbanization. The proposed optimal sampling approach selects 43 additional sampling sites with a high spatial estimation variance in bird diversity. Main conclusions Small‐scale variations dominate the total spatial variation of the observed diversity due to a lack of spatial information and insufficient sampling. However, simulations of bird diversity consistently capture the sampling statistics and spatial patterns of the observed bird diversity. The data thus accumulated can be used to understand the spatial patterns of bird diversity associated with different types of land cover and elevation, and to optimize sample selection. Co‐kriging combined with a genetic algorithm yields additional optimal sampling sites, which can be used to augment existing sampling points in future studies of bird diversity.