Effects Of Autocorrelation Research Articles

On the Role of Serial Correlation and Field Significance in Detecting Changes in Extreme Precipitation Frequency

AbstractStatistical trend analyses of observed precipitation (P) time series are key to validate theoretical arguments and climate projections suggesting that extreme P will increase in a warmer climate. Recent work warned about possible misinterpretation of trend tests if the presence of serial correlation and field significance are not considered. Here, we investigate these two aspects focusing on extreme P frequencies derived from 100‐year daily records of 1,087 worldwide gauges of the Global Historical Climatology Network. For this aim, we perform Monte Carlo experiments based on count time series generated with the Poisson integer autoregressive model and characterized by different sample size, level of autocorrelation, and trend magnitude. The main results are as follows. (a) Empirical autocorrelations are consistent with those of uncorrelated and stationary or nonstationary count time series, while empirical trends cannot be explained as the exclusive effect of autocorrelation; incorporating the impact of serial correlation in trend tests on extreme P frequency has then limited impacts on tests' performance. (b) Accounting for field significance improves interpretation of test results by limiting type‐I errors, but it also decreases test power; results of local tests could complement field significance outcomes and help identify weak trend signals where several trends of coherent sign are detected. (c) Based on these findings, evident patterns of statistically significant increasing (decreasing) trends emerge in central and eastern North America, northern Eurasia, and central Australia (southwestern America, southern Europe, and southern Australia). The methodological insights of this work support trend analyses of any hydroclimatic variable.

Innovative use of spatial regression models to predict the effects of green infrastructure on land surface temperatures

Understanding the complex and dynamic interplay and cumulative effects of green infrastructure (GI) and urban form on land surface temperatures (LST) is important to design and implement heat mitigation strategies. Past research has mostly employed two-dimensional (2D) indicators, simple correlations and conventional regression models using coarse-level analytical approaches that obviate spatial autocorrelation effects. For the first time, this study applies a holistic approach to evaluate GI and urban settings as complex dynamic systems. The objectives of this paper are to: (1) develop novel ‘spatially-based’ predictive models that account for spatial dependencies; (2) implement a fine-scale analytical unit (<50 m) for a more precise and accurate analysis; (3) incorporate the ‘multi-temporal’ diurnal and seasonal variations into predictions; and (4) propose the novel combination of 2D and 3D morphological, compositional and configurational parameters of GI and urban form derived from very high resolution (VHR) remotely-sensed data (<2m), using Sydney metropolitan region as case study. Results show a strong spatial association of LST at fine scale (<50 m) and spatial autocorrelation among residuals in traditional models. Spatial error model (SEM) exhibits a superior performance over conventional multivariate regression, however, results presented significant heteroscedasticity caused by the large temperature variability in certain areas, although this problem was partially solved. Future studies should incorporate unmeasured factors related to material-specific properties (i.e. albedo, emissivity), and capture the thermal variation within urban areas by segmenting datasets into zones with relatively homogenous thermal and physical properties. Overall, ground imperviousness mostly defines the LST profile of a place, with a relative warming effect of 0.23 °C and 0.61 °C during day; and 0.18 °C and 0.41 °C at night per 10% of area increment in winter and summer, respectively. The same increment in the proportion of water and trees contributes to a maximum LST reduction of 0.42–0.85 °C in summer, and 0.25–1.17 °C in winter; however, this causes an increase of nocturnal LST between 0.12 °C and 0.30 °C throughout the year. In general, the cooling effects from GI do not outweigh the warming effects from man-made surfaces. Compared to abundance, the spatial configuration of trees is less influential on LST. Ground sky view factor (GSVF), altitude and distance to coast are of relative importance in defining LST profiles. These results used to numerically simulate different greening scenarios at neighbourhood scale for Sydney; illustrating the potential of spatial models to define heat mitigation scenarios to inform urban design and planning policies.

A REVIEW OF SPATIAL ECONOMETRICS IN EXPLICIT LOCATION MODELLING OF COMMERCIAL PROPERTY MARKET

‘Location, location, location’ is a real property parlance mostly used to describe the influence of location in the property market. Location is mainly considered as the most significant influencer of commercial property prices. Location is modelled traditionally using hedonic pricing model by either proxy location dummies or distances relative to other neighbourhood features. This was shown to be inadequate due to spatial autocorrelation and heterogeneity inherent in spatial data, which jeopardises the estimates' consistency. Consequently, spatial econometrics is used to explicitly model location into property pricing by controlling spatial effects of autocorrelation and heterogeneity. Housing studies dominate the use of this approach with limited application in the commercial property market. This paper reviewed spatial econometrics and found that the commercial property market exhibits significant spatial dependence and heterogeneity. Accounting for such effects improves model accuracy significantly. It, therefore, recommends increase use of spatial econometrics in commercial property market modelling.

Ecological security of mineral resource-based cities in China: Multidimensional measurements, spatiotemporal evolution, and comparisons of classifications

Accurate assessments of the ecological security of mineral resource-based cities in China (MRBCCs) are one of the most important elements of improving comprehensive national security policies in the country and promoting high-quality development therein. To this end, an integrated method is proposed herein based on rough set-entropy weight method-spatial data analysis, and an ecological security assessment indicator system is constructed based on a pressure sensitivity–stability–external regulation nexus. The ecological security status of 108 MRBCCs was evaluated in multiple dimensions and based on their spatiotemporal evolution. The results show that the comprehensive ecological security of MRBCCs improved from 2010 to 2018, but it was still low. There exists strong heterogeneity in the comprehensive ecological security of different cities, and the gap will only increase over time. Additionally, the comprehensive ecological security of MRBCCs dependent on coal, petroleum, non-ferrous metal, and ferrous metal showed an upward trend from 2010 to 2018. Petroleum-dependent cities were the most stable and had the best external regulation, while comprehensive ecological security was the worst in coal-based cities. Recently, cities dependent on non-ferrous metals faced the highest ecological pressure, and both they and non-cities dependent on ferrous metals had relatively weak external regulations. Finally, the MRBCC had high levels of agglomerated ecological security, there was a strong spatial autocorrelation and promotion effect, and the agglomeration had clear spatial characteristics. The high- and low-value areas were in the eastern mineral resources and northeastern mineral resources cities, respectively. The temporal and spatial evolution characteristics of the ecological security of MRBCC are revealed in detail, providing scientific data and a quantitative reference to assist the Chinese government in accurate management of the green development of mineral resource cities.

Aerial invertebrate functional groups respond to landscape composition with only detritivores and predators responding to agri-environment scheme management

Landscape-scale agricultural intensification is a major driver of biodiversity loss. Highly mobile species, such as aerial invertebrates may be disproportionately affected by land cover at large spatial scales due to their ability to disperse. Assessing the efficacy of environmentally friendly farming, such as Agri-Environment Schemes (AES), therefore, requires establishing the influence of land cover beyond focal farms or fields, contextualising the impact of agricultural management. This study measured the response of aerial invertebrate functional groups to AES management in improved and semi-improved upland grasslands accounting for spatial autocorrelation and multi-scale effects of land cover within buffers varying from 100 m to 5 km at 88 paired fields. Total aerial invertebrate abundance and diversity, including detritivores, herbivores, nectivores, predators and parasitoids all responded to different land cover types at varying scales indicative of highly complex and idiosyncratic functional group responses to landscape.After accounting for spatial autocorrelation and multi-scale landscape composition, total aerial invertebrate abundance was unaffected by agricultural management but diversity was (5%) higher in AES than conventionally managed fields. Detritivore (mostly Mycetophilidae fungus gnat) diversity and abundance were higher in AES than conventional fields, but only on improved grasslands, while predator (adult Muscidae house fly, Scathophagidae dung fly and Dolichopodidae long-legged fly) diversity was lower but abundance higher in AES than conventional fields. Aerial invertebrate herbivores (Anthomyiidae root-maggot flies and Cecidomyiidae gall midges and gnats), nectivores (Syrphidae hoverflies and Apoidea bees) and parasitoids (Ichneumonidae wasps) responded to landscape composition, but not agricultural management. Synthesis and Applications: this study suggests that landscape composition drives aerial invertebrate functional group diversity and abundance at multiple spatial scales. Nevertheless, the responses of some functional groups (detritivores and predators) to local agricultural management suggest that AES management may contribute to the maintenance or enhancement of ecosystem service delivery (nutrient cycling and pest biocontrol). Explicitly accounting for multi-scale landscape effects is necessary when assessing the impact of agricultural management on highly mobile taxa.

Analysis of temporal trend of groundwater quality using nonparametric Mann-Kendall and Sen’s methods (Case study: Yazd-Ardakan Plain)

Groundwater is the most important water resource for agriculture, industry, and drinking, and therefore qualitative changes in program planning and sustainable management of water resources are necessary. This study aimed to evaluate the trend of groundwater quality parameters of some selected stations in Yazd province by using non-parametric Mann-Kendall and Sens Methods. For this purpose, the water quality data were gathered in 25 stations of Yazd-Ardakan plain during 2005-2014. The quality parameters used in this study were TH, Cl-, K+, Mg++, Ca++, Na%, TDS, SAR, Na+, HCO3-, SO42-, EC, pH, the total sum of cation and anion. Before analyzing the data, the effect of significant first-order autocorrelation was investigated from the data set. The results showed most water quality parameters tend to rise. 50.8% and 49.2% of groundwater quality time series show meaningful positive trends during wet and dry seasons, respectively. Also, 54.7% and 45.3% of groundwater quality time series show meaningful negative trends during wet and dry seasons, respectively. Due to the positive slope of the trend line for most quality parameters, water quality in this region tends to decreases. The maximum positive slope of the trend line belongs to EC, where observed downstream of the plain. The loss of groundwater level due to an increase in consumption can be one of the main reasons for decreasing the groundwater quality and salinity.

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method.

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural synchronization (INS) during social interactions, functional near-infrared spectroscopy (fNIRS) is one of the most popular techniques for hyperscanning naturalistic social interactions because of its relatively high spatial resolution, sound anatomical localization, and exceptionally high tolerance of motion artifacts. Previous fNIRS-based hyperscanning studies usually calculate a time-lagged INS using wavelet transform coherence (WTC) to describe the direction and temporal pattern of information flow between individuals. However, the results of this method might be confounded by the autocorrelation effect of the fNIRS signal of each individual. For addressing this issue, a method termed partial wavelet transform coherence (pWTC) was introduced, which aimed to remove the autocorrelation effect and maintain the high temporal-spectrum resolution of the fNIRS signal. In this study, a simulation experiment was performed first to show the effectiveness of the pWTC in removing the impact of autocorrelation on INS. Then, step-by-step guidance was offered on the operation of the pWTC based on the fNIRS dataset from a social interaction experiment. Additionally, a comparison between the pWTC method and the traditional WTC method and that between the pWTC method and the Granger causality (GC) method was drawn. The results showed that pWTC could be used to determine the INS difference between different experimental conditions and INS's directional and temporal pattern between individuals during naturalistic social interactions. Moreover, it provides better temporal and frequency resolution than the traditional WTC and better flexibility than the GC method. Thus, pWTC is a strong candidate for inferring the direction and temporal pattern of information flow between individuals during naturalistic social interactions.

Flexible Bayesian Dirichlet mixtures of generalized linear mixed models for count data

The need to model count data correctly calls for introducting a flexible yet robust model that can sufficiently handle various types of count data. Models such as Ordinary Least Squares (OLS) used in the past were considered unsuitable. The introduction of the Generalized Linear Model (GLM) and its various extensions was the first breakthrough recorded in modeling count data. Thishis article, propose the Bayesian Dirichlet process mixture prior of generalized linear mixed models (DPMglmm). Metropolis Hasting Monte Carlo Markov Chain (M-H MCMC) was used to draw parameters from target posterior distribution. The Iterated Weighted Least Square (IWLS) proposal was used to determine the acceptance probability in the M-H MCMC phase. Under and over-dispersed count data were simulated, 500 Burn-in was scanned to allow for stability in the chain. 100 thinning interval was allowed so as to nullify the possible effect of autocorrelation in the data due to the Monte Carlo procedure. The DPMglmm and other competing models were fitted to the simulated data, and real-life health insurance claims data sets. The results obtained showed that DPMglmm outperformed MCMCglmm, Bayesian Discrete Weibull, and four other frequentist models. This indicates that DPMglmm is flexible and can fit count data best, either under-dispersed or over-dispersed data.

SPATIAL PATTERN EVOLUTION AND DRIVING FACTORS OF PM2.5 CONCENTRATIONS IN THE GRAND CANAL REGION FROM 2000 TO 2018

Abstract. In recent years, air pollution related to PM2.5 has caused a significant impact on human health. The Grand Canal (GC) is not only a great Cultural heritage created in ancient China but also the longest and largest canal in the world. Based on remotely sensed PM2.5 gridded data in the GC region covering 2000 to 2018, we used the holistic methods of standard deviation ellipse, local moran index, slope trend analysis to reveal the spatiotemporal evolutions of PM2.5 concentrations in the GC regions and investigated the driving factors of PM2.5 concentrations by using the geographically weighted regression (GWR) model. Results show that (1) PM2.5 concentrations in the GC region exhibited an increasing trend and followed by a decreasing trend from 2000 to 2018 (the turning point emerged in 2010). (2) The standard deviation ellipse analyses show that the spatial distributions of PM2.5 concentrations featured more and more concentrated over time, whereas, after the year 2010, the distributions gradually featured scattered. (3) The concentrations of PM2.5 exhibited the strong effects of local spatial autocorrelation and areas with "high-high" agglomeration were mainly located in the central and west regions of the GC region and gradually expanded to the north over time. (4) The areas of regions with rapidly increasing in PM2.5 concentrations gradually decreased over time, however, those with rapidly decreasing in PM2.5 concentrations increased. (5) The influences of the natural factors and socio-economic factors on the distributions of PM2.5 concentrations varied spatially. In detail, the elevation was negatively correlated with PM2.5 concentrations, whereas an opposite relationship between industrial structure and PM2.5 concentrations was observed. The coefficients of rainfall, population density, GDP per capita and foreign investment show different results in positive and negative correlations depending on the position.

Exploring spatial interaction effects in freight trip generation model for intermediate and pure receiver establishment

Freight trip generation (FTG) plays an essential role in urban planning and traffic assessments for developing long-term transportation demand modeling. This study explored the spatial interaction effects on the development of FTG models for intermediate and pure receiver establishment using spatial econometric techniques. FTG models are developed by considering three types of spatial interaction effects such as exogenous, endogenous, and residual spatial autocorrelation effects. Initially, the supply chain variables and demographic characteristics are collected in Tiruchirappalli city, Tamil Nadu, India. The spatial indicators for intermediate and pure receiver establishment are identified by estimating spatial autocorrection of the variables using Moran’s I method. Further, the locational information of the study area is incorporated for representing the spatial neighborhood structure in the spatial weight matrix. The spatial interaction effects are assessed using spatial econometric modeling techniques. FTG models are developed using the linear Ordinary Least Square (OLS) technique and Spatial Econometric techniques such as Spatially Lagged X model (SLX), Spatial Durbin Model (SDM), and Spatial Durbin Error model (SDEM). The developed models are compared using the Bayesian approach, and the suitable FTG model is identified to explain the spatial interaction effects in more precisely. The results show that the SDEM is the most suitable FTG model than other spatial econometric models for both intermediate and pure receiver establishment. Spatial indicators such as own effects and spillover effects associated with the SDEM are estimated and interpreted for explaining the freight trip activity. Moreover, local spatial spillover significantly recognizes the urban fright trip generation for both intermediate and pure receiver establishment.

The effect of autocorrelation on the diagnostic procedures

Apart from quick detection of abnormal changes in a multivariate process, it is also critical to accurately identify the factors of abnormal changes after an out-of-control signal in multivariate statistical process control. Some diagnostic methods for fault identification were proposed by scholars on which one of the basic assumptions is that the process data are independent. The independent assumption is reasonable for many applications. However, with the development of industrial automation, the process data usually meet the phenomenon of autocorrelation. As it is well known, the autocorrelation affects detection ability of control chart. It is a natural question that is whether the autocorrelation affects the diagnostic performance of the diagnostic procedures. In the article, we propose the method adopted the analogy residual sequences and compare it with the performance of existing diagnostic methods for different shift sizes and various autocorrelation and cross-correlation structures. To limit the complexity, our discussion employs a first-order vector autoregressive process and focuses mainly on bivariate data in this article. The results of the simulation show the performances of diagnostic procedures were affected by autocorrelation and the impacts become greater as autocorrelation increases. On the other hand, the proposed method in the article can be free from the impacts of autocorrelation. Finally, a real example is also presented to demonstrate the implementation of the proposed method.

Bayesian Hetero-Elasticnet (A Gibbs Sampler Approach)

Combined heteroscedasticity and multicollinearity as dual non-spherical disturbances were experimented asymptotically. A Gibbs Sampler technique was used to investigate the asymptotic properties of hetero-elasticnet estimator with mean squares error (MSE) and bias as performance metrics. The seed was set to 12345; is set at ; Xs variables were generated as follow: the design matrix was generated from the multivariate normal distribution with mean &gt; 0 and variance . and are truncated with Harvey (1976) heteroscedastic error structure; are collinear covariate with pairwise correlation between 0.6 and 0.9, the sample sizes were 25, 100 and 1000. The number of replications of the experiment was set at 10,000 with burn-in of 1000 which specified the draws that were discarded to remove the effects of the initial values. The thinning was set at 5 to ensure the removal of the effects of autocorrelation in the MCMC simulation. The study found that there is consistency of estimator asymptotically as the sample sizes increases from 25 to 50 so also to 1000, the larger sample size depicted least bias. The estimator exhibited efficiency asymptotically as larger sample sizes depicted least mean squares error. The study therefore recommended Bayesian hetero-elasticnet when data exhibit both heteroscedasticity and multicollinearity.&#x0D; Keywords: Elasticnet; Bayesian Inference and Gibbs sampler

Building walkable and safe neighborhoods: Assessing the built environment characteristics for pedestrian safety in buffalo, NY

IntroductionIt is important to understand the relationship between the built environment and pedestrian safety to promote walking. Nevertheless, current literature insufficiently studied the impact of the built environment on pedestrian safety. There is few research considering spatial autocorrelation effects. This study aims to expand the investigation of conventional built environment correlates of pedestrian safety using the D variables from the walkability literature to further the discussions of the association of the built environment with both safety and walkability. MethodsData on pedestrian involved injuries were collected from the New York State Department of Transportation from 2012-2016. The results from the global Poisson regression (PR) models and the geographically weighted Poisson regression (GWPR) models were compared to show the spatially varying effects of built environment characteristics on pedestrian involved injuries. Data were analyzed in 2019. ResultsThe results from the GWPR models were more pronounced than the global PR models. The findings confirmed the association of pedestrian safety with the D variables. The number of intersections, Walk Score, and population density showed conflicting impacts on safety and walkability. The statistically significant association of Walk Score with pedestrian safety in the study enriches current research on the relationship between built environment and pedestrian safety. ConclusionsThis study expanded the traditional built environment variables used in pedestrian safety research with the D variables in walkability research to help understand the role of built environment characteristics for building walkable and safe neighborhoods. The findings also shed light on the significance of distance in environmental modification for safe and walkable communities.

Differences in Distance and Spatial Effects on Cross-Border E-Commerce and International Trade

Distance and space are important factors affecting international trade, but they have different effects on cross-border e-commerce (CBE) due to the creation of the Internet. This study utilizes spatial autocorrelation, the multi-dimension gravity model and the Spatial Durbin model to conduct an comparative analysis of international trade and CBE within one-belt one-road (BR) countries. Our study obtained several key findings. Firstly, the spatial autocorrelation effect which exists in international trade does not exist in CBE. Secondly, the geographical distance effect of CBE is not significant, which is different from that of international trade. Thirdly, CBE is affected by GDP, culture, policy and institution distances which is not entirely consistent with international trade. Finally, the Spatial Durbin model shows that the spillover effect of CBE and international trade are both significant in the inverse distance weight matrix. These findings provide not only important theoretical contributions but also a practical guide for Government policy makers of the BR and CBE.

The success of the invasive macrophyte Hydrilla verticillata and its interactions with the native Egeria najas in response to environmental factors and plant abundance in a subtropical reservoir

The invasion success of non-native species depends on a variety of factors, and abiotic characteristics of invasive ranges and their native populations can offer resistance to non-native species establishment. Reservoirs, compared to natural lakes, can facilitate submerged macrophyte invasion because they provide favorable abiotic conditions to macrophyte establishment and development. However, previous colonization of native species can hamper the development of invasive ones through priority effects. In this investigation we used large data-set obtained in the Itaipu Reservoir (Brazil/Paraguay) to assess the relationship between the abundance of the native submerged macrophyte Egeria najas and of the invasive Hydrilla verticillata and water level, littoral slope and Secchi disk depth. We also investigated how the concomitant and previous abundance of both macrophytes correlates. A Bayesian modeling was applied, controlling the effect of spatial-temporal autocorrelation. Our results indicate an important role of water transparency, littoral slope and water level oscillations in a short period of time (30 days) for the temporal dynamics of these species’ abundances. Fluctuations of water levels and littoral slope affected H. verticillata to a greater extent than E. najas. In addition, the abundance of each species was related with its own abundance in previous samplings, highlighting the possible importance of habitat suitability, vegetative propagule pressure and resistance structures for population recovery. In low abundances, there was a possible facilitation between both species, while high abundances probably increased competition. The great importance of water level oscillations on these species’ abundances indicate that this strategy can be used as management tool.

Finite sample performance of an estimator of process capability index Cpm for the autocorrelated data

Process capability index Cpm , sometimes called Taguchi index, is a widely used measure to assess the ability of a process to cluster around the target. In calculating Cpm , it is assumed that the process data are independent, however, in many processes this assumption is violated. Because the exact distribution of estimator of Cpm for autocorrelated data is unknown, in this article, we derive explicit expressions for the moments of and discuss its statistical properties in terms of these moments. The study shows that the estimator is, in general, positively biased. Further, the exact values of mean and standard deviations of are compared with the corresponding approximated values provided by earlier studies and it is found that the earlier studies overemphasize the effect of autocorrelation on especially for smaller sample sizes, used for calculating

Accounting for spatial autocorrelation is needed to avoid misidentifying trade-offs and bundles among ecosystem services

The identification of relationships between multiple ecosystem services (ES) (i.e. trade-offs, synergies and bundles) is essential for ES management. However, the identification of ES relationships may be susceptible to spatial autocorrelation — a statistical bias due to ES observations being related to each other across space. Spatial autocorrelation remains largely overlooked in the literature on ES relationships and its implications are not clear. Here we assess the implication of not accounting for spatial autocorrelation when determining ES relationships using four ES found in the city-state of Singapore. We quantify the ES relationships using some of the most common methods of determining relationships between ES: correlation, regression and principal component analysis. We then compare each method with the corresponding method that accounts for spatial autocorrelation. We found that accounting for spatial autocorrelation resulted in less statistically significant ES relationships, especially at finer resolutions, in correlations (33.3% less significant relationships) and regressions (50% less relationships). Depending on the spatial resolution, different ES were bundled when accounting for spatial autocorrelation when using principal component analysis. Our results suggest that not accounting for spatial autocorrelation in ES relationship studies is likely to result in the misidentification of ES trade-off, synergies and bundles. We thus recommend that future ES relationship studies consider the effects of spatial autocorrelation in their analyses.

Time series clustering based on time-varying Hurst exponent

We consider the problem of clustering time series which are assumed to possess the long term memory. We propose an approach based on combining the results obtained by applying different methods for estimating time-varying Hurst exponent and apply it to Euro exchange rates. Firstly, we fit AR-GARCH models to every time series to reduce bias of rescaled range analysis method. We only consider model with residuals, in which no autocorrelation and ARCH effect is present; among them we choose the model with the lowest value of the Bayesian information criterion. Afterwards, we estimate the Hurst exponent from the residuals by means of the rolling window approach using four different estimation methods. Vectors of Hurst exponents are clustered for each of the four cases and the clusters are compared in order to obtain the final clustering.

Patterns and drivers of phylogenetic structure of pteridophytes in China

AbstractAimPrevious studies have found strikingly different geographical patterns of phylogenetic diversity of gymnosperms and angiosperms, which have been suggested to result, in part, from the greater age of the former. To assess the effects of clade age on phylogenetic structure, we assessed phylodiversity patterns of pteridophytes (lycophytes and ferns), which combine ancient and modern lineages, across China.LocationChina.Time periodPresent day.TaxonPteridophytes.MethodsWe divided China into 66 regions and collated species lists of pteridophytes for each region. We then related taxonomic and phylogenetic diversity, and their related metrics, to six climatic variables for pteridophytes as a whole and for different evolutionary clades. We examined the effect of spatial autocorrelation among regional floras on the results.ResultsWe found that when all pteridophytes or ferns were considered, the patterns resembled those of gymnosperms, presumably reflecting the effects of several ancient global mass extinctions and the relict distribution of old families. In contrast, when we considered only the modern Polypodiales radiation, which accounts for three‐quarters of current fern diversity and is about as old as the angiosperm radiation, we found a pattern similar to that of angiosperms. This pattern is consistent with the tropical niche conservatism hypothesis and suggests that both major groups of land plants have a broadly similar evolutionary trajectory, with most families originating in tropical conditions and few families adapting to cold and arid conditions.Main conclusionsOur study shows that the phylogenetic structures of more recently evolved and diversified clades of pteridophytes are fairly consistent with the tropical niche conservatism hypothesis, suggesting that the age of the taxon, its physiological adaptations and the global climatic changes that it experienced during its evolutionary history are reflected in the composition of current plant assemblages.

Analysis of correlations between local geographic atrophy growth rates and local OCT angiography-measured choriocapillaris flow deficits.

The purpose of this study is to quantitatively assess correlations between local geographic atrophy (GA) growth rates and local optical coherence tomography angiography (OCTA)-measured choriocapillaris (CC) flow deficits. Thirty-eight eyes from 27 patients with GA secondary to age-related macular degeneration (AMD) were imaged with a commercial 1050 nm swept-source OCTA instrument at 3 visits, each separated by ∼6 months. Pearson correlations were computed between local GA growth rates, estimated using a biophysical GA growth model, and local OCTA CC flow deficit percentages measured along the GA margins of the baseline visits. The p-values associated with the null hypothesis of no Pearson correlation were estimated using a Monte Carlo permutation scheme that incorporates the effects of spatial autocorrelation. The null hypothesis (Pearson's ρ = 0) was rejected at a Benjamini-Hochberg false discovery rate of 0.2 in 15 of the 114 visit pairs, 11 of which exhibited positive correlations; even amongst these 11 visit pairs, correlations were modest (r in [0.30, 0.53]). The presented framework appears well suited to evaluating other potential imaging biomarkers of local GA growth rates.