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Meta‐analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta‐analyses. Here, we first present a systematic literature survey on the frequency and treatment of missing data in published ecological meta‐analyses showing that the majority of meta‐analyses encountered incompletely reported studies. We then simulated meta‐analysis data sets to investigate the performance of 14 options to treat or impute missing SDs and/or SSs. Performance was thereby assessed using results from fully informed weighted analyses on (hypothetically) complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size‐based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SDs and SSs. The performance of different imputation methods depends on the structure of the meta‐analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. In a best‐case scenario, which assumes that SDs and/or SSs are both missing at random and are unrelated to effect sizes, our simulations show that the imputation of up to 90% of missing data still yields grand means and confidence intervals that are similar to those obtained with fully informed weighted analyses. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta‐analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness. Meta‐analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta‐analyses. We present a systematic literature survey on the frequency and treatment of missing data in published ecological meta‐analyses. Simulating the effect of 14 different options to treat missing data in meta‐analysis, we show that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies.

Background The aim of this retrospective study is to compare photon-counting detector computed tomography (PCD-CT) derived virtual non-contrast (VNC) images of the liver reconstructed from both arterial and portal venous phase using conventional and liver-specific VNC algorithm to true non-contrast images, in context of the body mass index (BMI). Methods VNC images reconstructed from multiphase (non-contrast, arterial and portal venous phase) PCD-CT scans performed between April 2021 and February 2023 were analysed retrospectively. For each patient, four VNC series were generated: two series (arterial and portal venous) using a conventional VNC algorithm (VNC conv art ; VNC conv pv ) and two using a liver-specific “Liver VNC” algorithm (VNC Liver art ; VNC Liver pv ). Regions of interest were placed in the left and right liver lobes and in the spleen, avoiding large vessels and focal lesions. The VNC CT-values were then compared to those of the corresponding true non-contrast images (TNC). The subsequent analysis involved the calculation of both correlation and mean offsets. The median split was utilised to ascertain distinct cohorts of patients with elevated and reduced body mass indices. These cohorts were then subjected to a comparative analysis of attenuation values to discern potential disparities between them. The results were compared by using parametric and non-parametric tests; Pearson’s correlation coefficient was employed. Bland-Altman plots were utilised to visually assess the agreement between results and Passing-Bablok regression, thereby quantifying the observed agreement. Results The study population comprised 42 patients (mean age 70.0 ± 10.2 years, 33 males). Mean offsets between TNC and VNC conv art was 0.62 ± 5.23 HU, TNC-VNC conv pv 1.24 ± 6.67 HU, TNC-VNC Liver art -0.94 ± 5.59 and TNC-VNC Liver pv -0.35 ± 6.99 with no significant difference. Significant differences were found for VNC conv art , VNC conv pv and VNC Liver art images regarding spleen attenuation. Bland-Altman plots demonstrated good agreement and the absence of any systematic difference in liver attenuation. As for the TNC-VNC conv art , TNC-VNC conv pv , TNC-VNC Liver art and TNC-VNC Liver pv variables, strong correlations were obtained (Pearson’s coefficient: 0.79, 0.69, 0.79 and 0.7, all p  < 0.001). The investigation revealed no statistically significant disparities between the BMI groups with respect to the mean offset of liver density ( p-value :TNC-VNC conv art 0.51; VNC conv pv 0.61; VNC Liver art 0.68; VNC Liver pv 0.45). Furthermore, no significant offset between TNC and VNC images was detected within each BMI group. A Passing-Bablok regression analysis revealed no systematic or proportional difference between the two methods. Conclusion It is evident that PCD-CT-derived VNC images generally constitute a corresponding alternative to TNC images. However, caution is advised in the interpretation of images, as there are outliers with differences exceeding 15 HU are present. In general, the mean values obtained from the analysis of, VNC images reconstructed from arterial and portal venous phases employing both the liver-specific and general VNC reconstruction algorithm did not demonstrate any clincially significant difference when compared with TNC images. Furthermore, no significant discrepancy was observed in the utilisation of the conventional and the liver-specific algorithm. The findings of this study demonstrated that, within the limitations of the study, the patients’ BMI did not have a significant impact on the VNC images.

Plant species richness is essential for ecosystem functioning, resilience and ecosystem services, yet is globally threatened by anthropogenic land use, including management and modification of the natural environment. At broad scales, land-use effects are often simply modelled by habitat loss, assuming that transformed land becomes completely inhospitable for naturally occurring species. Further, estimates of species losses are flawed by the common assumption of a universal slope of the species–area curve, typically ranging from 0.15 to 0.35. My PhD dissertation consists of a global species–area analysis, a meta-analysis about land-use effects on plant species richness and an approach to integrate these land-use effects in a countryside species–area model. Overall, my PhD research contributes to a deeper understanding of species–area relationships and how patterns of species richness at macroscales are driven by land use. It proposes a model to predict species richness patterns of vascular plants that overcomes limitations of previous models.

Background/Objectives: Hepatocellular carcinoma (HCC) is the most common primary malignant tumour of the liver. In a cirrhotic liver, each nodule larger than 10 mm demands further work-up using CT or MRI. The Liver Imaging Reporting and Data System (LI-RADS) is still based on visual assessment and measurements. The purpose of this study was to evaluate whether semi-automated quantification of visual LR-5 lesions is appropriate and can objectify HCC classification for personalized radiomic research. Methods: A total of 52 HCC patients (median age 67 years, 17% females, 83% males) from a retrospective data collection were evaluated visually and compared by the results using an oncology software with features of LI-RADS-based structured tumour evaluation and documentation, semi-automated tumour segmentation, and texture analysis. Results: Software-based evaluation of non-rim arterial-phase hyperenhancement (APHE) and non-peripheral washout, as well as the LI-RADS-score, showed no statistically significant differences compared with visual assessment (p = 0.2, 0.7, 0.17), with a consensus between a human reader and the software approach in 98% (APHE), 89% (washout), and 93% (threshold growth) of cases, respectively. The software provided automated LI-RADS classification, structured reporting, and quantitative features for HCC registries and radiomic research. Conclusions: The presented work may serve as an outlook for LI-RADS-based automated qualitative and quantitative evaluation. Future research may show if texture analysis can be used to foster personalized medical approaches in HCC.

Species–area relationships (SARs) provide an avenue to model patterns of species richness and have recently been shown to vary substantially across regions of different climate, vegetation, and land cover. Given that a large proportion of the globe has been converted to agriculture, and considering the large variety in agricultural management practices, a key question is whether global SARs vary across gradients of agricultural intensity. We developed SARs for mammals that account for geographic variation in biomes, land cover and a range of land-use intensity indicators representing inputs (e.g. fertilizer, irrigation), outputs (e.g. yields) and system-level measures of intensity (e.g. human appropriation of net primary productivity – HANPP). We systematically compared the resulting SARs in terms of their predictive ability. Our global SAR with a universal slope was significantly improved by the inclusion of any one of the three variable types: biomes, land cover, and land-use intensity. The latter, in the form of human appropriation of net primary productivity (HANPP), performed as well as biomes and land-cover in predicting species richness. Other land-use intensity indicators had a lower predictive ability. Our main finding that land-use intensity performs as well as biomes and land cover in predicting species richness emphasizes that human factors are on a par with environmental factors in predicting global patterns of biodiversity. While our broad-scale study cannot establish causality, human activity is known to drive species richness at a local scale, and our findings suggest that this may hold true at a global scale. The ability of land-use intensity to explain variation in SARs at a global scale had not previously been assessed. Our study suggests that the inclusion of land-use intensity in SAR models allows us to better predict and understand species richness patterns.

Forest products provide an important source of income and wellbeing for rural smallholder communities across the tropics. Although tropical forest products frequently become over-exploited, only few studies explicitly address the dynamics of degradation in response to socio-economic drivers. Our study addresses this gap by analyzing the factors driving changes in tropical forest products in the perception of rural smallholder communities. Using the poverty and environment network global dataset, we studied recently perceived trends of forest product availability considering firewood, charcoal, timber, food, medicine, forage and other forest products. We looked at a pan-tropical sample of 233 villages with forest access. Our results show that 90% of the villages experienced declining availability of forest resources over the last five years according to the informants. Timber and fuelwood together with forest foods were featured as the most strongly affected, though with marked differences across continents. In contrast, availability of at least one main forest product was perceived to increase in only 39% of the villages. Furthermore, the growing local use of forest resources is seen as the main culprit for the decline. In villages with both growing forest resource use and immigration-vividly illustrating demographic pressures-the strongest forest resources degradation was observed. Conversely, villages with little or no population growth and a decreased use of forest resources were most likely to see significant forest-resource increases. Further, villages are less likely to perceive resource declines when local communities own a significant share of forest area. Our results thus suggest that perceived resource declines have only exceptionally triggered adaptations in local resource-use and management patterns that would effectively deal with scarcity. Hence, at the margin this supports neo-Malthusian over neo-Boserupian explanations of local resource-use dynamics.

Although habitat loss is the predominant factor leading to biodiversity loss in the Anthropocene 1 , 2 , exactly how this loss manifests—and at which scales—remains a central debate 3 – 6 . The ‘passive sampling’ hypothesis suggests that species are lost in proportion to their abundance and distribution in the natural habitat 7 , 8 , whereas the ‘ecosystem decay’ hypothesis suggests that ecological processes change in smaller and more-isolated habitats such that more species are lost than would have been expected simply through loss of habitat alone 9 , 10 . Generalizable tests of these hypotheses have been limited by heterogeneous sampling designs and a narrow focus on estimates of species richness that are strongly dependent on scale. Here we analyse 123 studies of assemblage-level abundances of focal taxa taken from multiple habitat fragments of varying size to evaluate the influence of passive sampling and ecosystem decay on biodiversity loss. We found overall support for the ecosystem decay hypothesis. Across all studies, ecosystems and taxa, biodiversity estimates from smaller habitat fragments—when controlled for sampling effort—contain fewer individuals, fewer species and less-even communities than expected from a sample of larger fragments. However, the diversity loss due to ecosystem decay in some studies (for example, those in which habitat loss took place more than 100 years ago) was less than expected from the overall pattern, as a result of compositional turnover by species that were not originally present in the intact habitats. We conclude that the incorporation of non-passive effects of habitat loss on biodiversity change will improve biodiversity scenarios under future land use, and planning for habitat protection and restoration. Analysis of 123 studies of assemblage-level abundances of focal taxa from fragmented habitats finds that increasing fragmentation has a disproportionately large effect on biodiversity loss, supporting the ecosystem decay hypothesis.

Objectives: Prediction of lung function deficits following pulmonary infection is challenging and suffers from inaccuracy. We sought to develop machine-learning models for prediction of post-inflammatory lung changes based on COVID-19 recovery data. Methods: In the prospective CovILD study (n = 420 longitudinal observations from n = 140 COVID-19 survivors), data on lung function testing (LFT), chest CT including severity scoring by a human radiologist and density measurement by artificial intelligence, demography, and persistent symptoms were collected. This information was used to develop models of numeric readouts and abnormalities of LFT with four machine learning algorithms (Random Forest, gradient boosted machines, neural network, and support vector machines). Results: Reduced DLCO (diffusion capacity for carbon monoxide <80% of reference) was found in 94 (22%) observations. Those observations were modeled with a cross-validated accuracy of 82–85%, AUC of 0.87–0.9, and Cohen’s κ of 0.45–0.5. No reliable models could be established for FEV1 or FVC. For DLCO as a continuous variable, three machine learning algorithms yielded meaningful models with cross-validated mean absolute errors of 11.6–12.5% and R2 of 0.26–0.34. CT-derived features such as opacity, high opacity, and CT severity score were among the most influential predictors of DLCO impairment. Conclusions: Multi-parameter machine learning trained with demographic, clinical, and artificial intelligence chest CT data reliably and reproducibly predicts LFT deficits and outperforms single markers of lung pathology and human radiologist’s assessment. It may improve diagnostic and foster personalized treatment.

Summary Plant diversity is globally threatened by anthropogenic land use including management and modification of the natural environment. At regional and local scales, numerous studies world‐wide have examined land use and its effects on plant diversity, but evidence for declining species diversity is mixed. This is because, first, land use comes in many variations, hampering comparisons of studies. Second, land use directly affects the environment, but indirect effects extend beyond the boundaries of the land in use. Third, land‐use effects greatly depend on the environmental, historical and socio‐economic context. To evaluate the generality and variation of studies’ findings about land‐use effects, we undertook a quantitative synthesis using meta‐analytic techniques. Using 572 effect sizes from 375 studies distributed globally relating to 11 classes of land use, we found that direct and indirect effects of land use on plant diversity (measured as species richness) are variable and can lead to both local decreases and increases. Further, we found evidence (best AIC model) that land‐use‐specific covariables mostly determine effect‐size variation and that in general land‐use effects differ between biomes. Synthesis and applications. This extensive synthesis provides the most comprehensive and quantitative overview to date about the effects of the most widespread and relevant land‐use options on plant diversity and their covariables. We found important covariables of specific land‐use classes but little evidence that land‐use effects can be generally explained by their environmental and socio‐economic context. We also found a strong regional bias in the number of studies (i.e. more studies from Europe and North America) and highlight the need for an overarching and consistent land‐use classification scheme. Thereby, our study provides a new vantage point for future research directions. This extensive synthesis provides the most comprehensive and quantitative overview to date about the effects of the most widespread and relevant land‐use options on plant diversity and their covariables. We found important covariables of specific land‐use classes but little evidence that land‐use effects can be generally explained by their environmental and socio‐economic context. We also found a strong regional bias in the number of studies (i.e. more studies from Europe and North America) and highlight the need for an overarching and consistent land‐use classification scheme. Thereby, our study provides a new vantage point for future research directions. Editor's Choice

1. Plant diversity is globally threatened by anthropogenic land use including management and modification of the natural environment. At regional and local scales, numerous studies world-wide have examined land use and its effects on plant diversity, but evidence for declining species diversity is mixed. This is because, first, land use comes in many variations, hampering comparisons of studies. Second, land use directly affects the environment, but indirect effects extend beyond the boundaries of the land in use. Third, land-use effects greatly depend on the environmental, historical and socio-economic context. 2. To evaluate the generality and variation of studies' findings about land-use effects, we undertook a quantitative synthesis using meta-analytic techniques. 3. Using 572 effect sizes from 375 studies distributed globally relating to 11 classes of land use, we found that direct and indirect effects of land use on plant diversity (measured as species richness) are variable and can lead to both local decreases and increases. Further, we found evidence (best AIC model) that land-use-specific covariables mostly determine effectsize variation and that in general land-use effects differ between biomes. 4. Synthesis and applications. This extensive synthesis provides the most comprehensive and quantitative overview to date about the effects of the most widespread and relevant land-use options on plant diversity and their covariables. We found important covariables of specific land-use classes but little evidence that land-use effects can be generally explained by their environmental and socio-economic context. We also found a strong regional bias in the number of studies (i.e. more studies from Europe and North America) and highlight the need for an overarching and consistent land-use classification scheme. Thereby, our study provides a new vantage point for future research directions.