Challenges in using land use and land cover data for global change studies

Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscapes.     

Spring airborne pollen data in two sites in Trentino (Northern Italy): a comparison with meteorological data

In this paper airborne pollen concentration is compared to meteorological data of Trento and S. Michele all’Adige, two sites in the Adige Valley, in Trentino (North Italy). Pollen ofCorylus, Alnus, Betula andOstrya, four winter-spring flowering plants are considered. Pollen sampling was carried out in 1996 by volumetric Hirst-type samplers. For all pollen types considered, maximum pollen concentration coincided in both stations and there was a good overlap of the main pollen season length; the pollen curves of S. Michele a/A and Trento showed a highly positive correlation. The daily airborne pollen concentrations, defined as the number of pollen grains per cubic meter of air (P/m³), were compared to daily meteorological data: minimum and maximum air temperature (°C), average relative humidity (%), precipitation (mm), global incident radiation (cal/cm²), average wind direction (°) and wind speed (m/s). A highly positive correlation was found forCorylus and maximum temperature in both monitoring stations.Betula was positively, whereasOstrya was negatively correlated to relative humidity. With this first analytical approach sharp differences in the atmospheric pollen presence between the stations located at Trento and S. Michele all’Adige were not found.     

Quantifying reliability and data deficiency in global vertebrate population trends using the Living Planet Index

Global biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. However, without a basis for real-world comparison, there is no way to directly assess an indicator's accuracy or reliability. Instead, a modelling approach can be used. We developed a model of trend reliability, using simulated datasets as stand-ins for the “real world”, degraded samples as stand-ins for indicator datasets (e.g., the Living Planet Database), and a distance measure to quantify reliability by comparing partially sampled to fully sampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, whereas revisiting previously studied populations is a quick and efficient way to improve trend reliability until new long-term studies can be completed and made available.     

Multiple imputation for multivariate data with missing and below-threshold measurements: time-series concentrations of pollutants in the Arctic

Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week-long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete-data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple-imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets.     

Rangeland vegetation dynamics and moisture availability in Tunisia: an investigation using satellite and meteorological data

Improved knowledge of the interactions between regional climatic patterns and vegetation dynamics are necessary for predicting the future impacts of climate change on vegetation and biogeochemical processes. This paper describes how Normalized Difference Vegetation Index (NDVI) images generated from Advanced Very High Resolution Radiometer (AVHRR) satellite data were used to investigate the dynamics of rangeland vegetation in Tunisia. The NDVI images provided information about intra- and inter-annual variations in vegetation over nine growing seasons (1983–1992). Comparison of the NDVI data with field-collected ecological parameters for nine individual field sites indicated a strong relationship between the NDVI and percentage vegetation cover. The relationship between biomass measurements and NDVI was, however, less strong. Rainfall and NDVI data for each field site were compared, and significant relationships were found between the two. These indicated that there was a delay in the vegetation response to rainfall. In addition, the NDVI data showed that the vegetation at some of the field sites remained active throughout the summer although there was no rainfall during this period. TuMERT (Tunisian Model to Estimate Rangeland Transpiration), a simple water-balance model, was developed to estimate the amount of rainfall available for use by the vegetation during transpiration. The estimates of actual transpiration derived from TuMERT were found to be more strongly correlated with the AVHRR-NDVI measurements than the rainfall data.     

Shifting trends: detecting environmentally mediated regulation in long-lived marine vertebrates using time-series data

Assessing the status and trends in animal populations is essential for effective species conservation and management practices. However, unless time-series abundance data demonstrate rapid and reliable fluctuations, objective appraisal of directionality of trends is problematic. We adopted a multiple-working hypotheses approach based on information-theoretic and Bayesian multi-model inference to examine the population trends and form of intrinsic regulation demonstrated by a long-lived species, the southern elephant seal. We also determined the evidence for density dependence in 11 other well-studied marine mammal species. (1) We tested the type of population regulation for elephant seals from Marion Island (1986–2004) and from 11 other marine mammal species, and (2) we described the trends and behavior of the 19-year population time series at Marion Island to identify changes in population trends. We contrasted five plausible trend models using information-theoretic and Bayesian-inference estimates of model parsimony. Our analyses identified two distinct phases of population growth for this population with the inflexion occurring in 1998. Thus, the population decreased between 1986 and 1997 (−3.7% per annum) and increased between 1997 and 2004 (1.9% per annum). An index of environmental stochasticity, the Southern Oscillation Index, explained some of the variance in r and N. We determined analytically that there was good evidence for density dependence in the Marion Island population and that density dependence was widespread among marine mammal species (67% of species showed evidence for population regulation). This approach demonstrates the potential functionality of a relatively simple technique that can be applied to short time series to identify the type of regulation, and the uncertainty associated with the phenomenon, operating in populations of large mammals.     

Survival analysis in clinical trials: past developments and future directions

The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association 53, 457-481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report 50, 163-170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods.     

Importance of solitarious desert locust population dynamics: lessons from historical survey data in Algeria

The desert locust, Schistocerca gregaria (Forskål) (Orthoptera: Acrididae), is a major pest and well known in its gregarious phase. However, it is not well understood during recession periods, when the solitarious phase populations are discrete. Nonetheless, these populations are at the origin of the invasions when ecological conditions become favourable. This lack of knowledge of the solitarious phase individuals impedes effective preventive management of this pest. Archive data collected in Algeria from 1980 to 2011 were used to analyse solitarious population dynamics across the Algerian Sahara where some outbreak areas are located that play a major role in the invasion process. The results confirm previous empirical observations on solitarious population dynamics. First, a clear difference could be documented between the northern and southern Saharan regions of Algeria concerning the locust dynamics and the impact of environmental conditions. The importance of runoff was clear to create suitable habitats over a long period and to very distant places from rainy areas. Second, a link, on an annual basis, between green vegetation and presence of solitarious locusts was found. Third, statistical relationships between various locations demonstrated a clear regional dynamics. Our study confirmed the importance of migrations of solitarious populations among Algerian regions and more generally within the recession area of this species. The operational implications of these findings are multiple. First, they confirm the need of a flexible and scalable preventive system during the year, from 1 year to another and with a clear distinction between the northern and southern Saharan areas of Algeria. Second, they also confirm the necessity for the inclusion of wadis and soil moisture estimations from remote sensing in geographic information systems for preventive management. And third, they clearly illustrate the importance to target solitarious locusts for more efficient preventive survey operations.     

An updated global data set for diet preferences in terrestrial mammals: testing the validity of extrapolation

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Monitoring surface chemical changes in the bacterial cell wall: multivariate analysis of cryo-x-ray photoelectron spectroscopy data

Gram-negative bacteria can alter the composition of the lipopolysaccharide (LPS) layer of the outer membrane as a response to different growth conditions and external stimuli. These alterations can, for example, promote attachment to surfaces and biofilm formation. The changes occur in the outermost layer of the cell and may consequently influence interactions between bacterial cells and surrounding host tissue, as well as other surfaces. Microscopic analyses, fractionation of bacterial cells, or other traditional microbiological assays have previously been used to study these alterations. These methods can, however, be time consuming and do not always give detailed chemical information about the bacterial cell surface. We here present an analytical method that provides chemical information on the outermost portion of bacterial cells with respect to protein, peptidoglycan, lipid, and polysaccharide content. The method involves cryo-x-ray photoelectron spectroscopy analyses of the outermost portion (within ～10 nm of the surface) of intact bacterial cells followed by a multivariate curve resolution analysis of carbon spectra. It can be used as a tool for characterizing and monitoring variations in the chemical composition of bacterial cell walls or of isolated outer membrane vesicles, variations that result from e.g. mutations or external stimuli. The method enabled us to predict accurately the alterations in polysaccharide content and surface chemistries of a set of well characterized Escherichia coli LPS mutants. The described approach may moreover be applied to monitor surface chemical composition of other biological samples.     

Conservation planning and viability: problems associated with identifying priority sites in Swaziland using species list data

Conservation planning assessments based on species atlas data are known to select planning units containing ecotones because these areas are relatively species‐rich. However, this richness is often dependent on the presence of adjoining core habitat, so populations within these ecotones might not be viable. This suggests that atlas data may also fail to distinguish between planning units that are highly transformed by agriculture or urbanization with those from neighbouring untransformed units. These highly transformed units could also be identified as priority sites, based solely on the presence of species that require adjoining habitat patches to persist. This potential problem was investigated using bird and mammal atlas data from Swaziland and a landcover map and found that: (i) there was no correlation between planning unit species richness and proportion of natural landcover for both taxa; (ii) the priority areas that were identified for both birds and mammals were no less transformed than if the units had been chosen at random and (iii) an approach that aimed to meet conservation targets and minimize transformation levels failed to identify more viable priority areas. This third result probably arose because 4.8% of the bird species and 22% of the mammal species were recorded in only one planning unit, reducing the opportunity to choose between units when aiming to represent each species. Therefore, it is suggested that using species lists to design protected area networks at a fine spatial scale may not conserve species effectively unless population viability data are explicitly included in the analysis.     

New features in the dendroTools R package: Bootstrapped and partial correlation coefficients for monthly and daily climate data

Climate-growth relationships are usually analysed using monthly climate data. The dendroTools R package also provides methodological approaches that enable climate-growth analysis for daily climate data. Such analysis reveals more complete climate signal patterns. In this article, new functions of the dendroTools R package are presented. Partial correlation coefficients are now implemented and can be used to calculate the strength of a linear relationship between two variables, while controlling for a third variable. Bootstrapped correlations can then be used to provide insights into the confidence intervals of statistical estimates. The calculation of partial and bootstrapped correlations is available for daily and monthly data. Finally, data transformation, S3 generic plotting and summary functions are also presented here.     

Climate and the inter‐annual variability of fire in southern Africa: a meta‐analysis using long‐term field data and satellite‐derived burnt area data

Aim This study investigates inter‐annual variability in burnt area in southern Africa and the extent to which climate is responsible for this variation. We compare data from long‐term field sites across the region with remotely sensed burnt area data to test whether it is possible to develop a general model. Location Africa south of the equator. Methods Linear mixed effects models were used to determine the effect of rainfall, seasonality and fire weather in driving variation in fire extent between years, and to test whether the effect of these variables changes across the subcontinent and in areas more and less impacted by human activities. Results A simple model including rainfall and seasonality explained 40% of the variance in burnt area between years across 10 different protected areas on the subcontinent, but this model, when applied regionally, indicated that climate had less impact on year‐to‐year variation in burnt area than would be expected. It was possible to demonstrate that the relative importance of rainfall and seasonality changed as one moved from dry to wetter systems, but most noticeable was the reduction in climatically driven variability of fire outside protected areas. Inter‐annual variability is associated with the occurrence of large fires, and large fires are only found in areas with low human impact. Main conclusions This research gives the first data‐driven analysis of fire–climate interactions in southern Africa. The regional analysis shows that human impact on fire regimes is substantial and acts to limit the effect of climate in driving variation between years. This is in contrast to patterns in protected areas, where variation in accumulated rainfall and the length of the dry season influence the annual area burnt. Global models which assume strong links between fire and climate need to be re‐assessed in systems with high human impact.     

Gender and age-specific reference intervals of common biochemical analytes in Chinese population: Derivation using real laboratory data

BackgroundIndirect sampling methods are not only inexpensive but also efficient for establishing reference intervals (RIs) using clinical data. This study was conducted to select fully normal records to establish ageand gender-specific RIs for common biochemical analytes by laboratory data mining.MethodsIn total, 280,206 records from 2014 to 2018 were obtained from Peking Union Medical College Hospital. Common biochemical analytes total protein, albumin, total bilirubin (TBil), direct bilirubin (DBil), alanine aminotransferase (ALT), glutamyltranspeptidase (GGT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), potassium, sodium, chlorine, calcium, urea, glucose, uric acid (UA), inorganic phosphorus, creatinine (Cr), total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol] were measured using an automatic analyzer. Sources of variation were identified by multiple regression analysis. The 2.5th and 97.5th percentiles were calculated as the lower and upper limits of the RIs, respectivelyResultsGender was the major source of variation among the 13 common biochemical analytes with an rp > 0.15. In contrast to the value listed in the WS/T 404, nearly all RIs established in this study were significantly narrower. Furthermore, age-specific RIs should be determined for DBil, LDH, and urea, whereas gender-specific RIs are suggested for GGT, LDH, and urea.ConclusionsWe recommend that gender-specific RIs should be established for ALT, AST, GGT, DBil, TBil, UA, and Cr as well as genderand age-specific RIs for urea and ALP. Through indirect sampling, ageand gender-specific RIs for common biochemical analytes were established and analyzed.     

Association between metabolic syndrome and participation in colorectal cancer screening in Japan: A retrospective cohort analysis using LIFE study data

BackgroundPeople with metabolic syndrome have an elevated risk of developing colorectal cancer (CRC), and are recommended to undergo cancer screening. This study examined the association between metabolic syndrome and CRC screening participation in Japan.MethodsThis retrospective cohort study was conducted using insurance claims data, health checkup data, and cancer screening data from a Japanese city. The study population comprised persons aged 40–74 years who had undergone health checkups between fiscal years (FY) 2016 and 2019. The exposure was metabolic syndrome risk (high risk, moderate risk, and no risk) as determined during health checkups. The outcome was CRC screening participation. Logistic regression analyses were performed to examine the associations between metabolic syndrome risk and CRC screening participation.ResultsWe analyzed 20,558 people in the FY2016 cohort, 19,065 people in the FY2017 cohort, 17,496 people in the FY2018 cohort, and 15,647 people in the FY2019 cohort. The odds of CRC screening participation were significantly lower in the moderate-risk group (P < 0.05) in all FYs except FY2019 and the high-risk group (P < 0.001) in all FYs when compared with the no-risk group. When analyzed according to age group, older persons aged 65–74 years generally had significantly lower odds of CRC screening participation than persons aged 40–49 years across all metabolic syndrome risk groups.ConclusionThis is the first study from Japan to show that people with metabolic syndrome, especially older persons aged 65–74 years, are less likely to undergo CRC screening than people without metabolic syndrome. These findings indicate a need to develop and implement age-specific measures to increase cancer screening uptake among persons with metabolic syndrome.     

Cooperative effects in hydrogen-bonding of protein secondary structure elements: a systematic analysis of crystal data using Secbase

A systematic analysis of the hydrogen-bonding geometry in helices and beta sheets has been performed. The distances and angles between the backbone carbonyl O and amide N atoms were correlated considering more than 1500 protein chains in crystal structures determined to a resolution better than 1.5 A. They reveal statistically significant trends in the H-bond geometry across the different secondary structural elements. The analysis has been performed using Secbase, a modular extension of Relibase (Receptor Ligand Database) which integrates information about secondary structural elements assigned to individual protein structures with the various search facilities implemented into Relibase. A comparison of the mean hydrogen-bond distances in alpha helices and 3(10) helices of increasing length shows opposing trends. Whereas in alpha helices the mean H-bond distance shrinks with increasing helix length and turn number, the corresponding mean dimension in 3(10) helices expands in a comparable series. Comparing similarly the hydrogen-bond lengths in beta sheets there is no difference to be found between the mean H-bond length in antiparallel and parallel beta sheets along the strand direction. In contrast, an interesting systematic trend appears to be given for the hydrogen bonds perpendicular to the strands bridging across an extended sheet. With increasing number of accumulated strands, which results in a growing number of back-to-back piling hydrogen bonds across the strands, a slight decrease of the mean H-bond distance is apparent in parallel beta sheets whereas such trends are obviously not given in antiparallel beta sheets. This observation suggests that cooperative effects mutually polarizing spatially well-aligned hydrogen bonds are present either in alpha helices and parallel beta sheets whereas such influences seem to be lacking in 3(10) helices and antiparallel beta sheets.