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Question: What are the drivers of structural changes and mortality in oak— beech forests over 50 yrs of natural succession? Location: Twelve unmanaged forest sites, comprising a large environmental gradient in the Swiss lowlands. Method: By using repeated inventory data from more than 17 600 individually tagged trees, the dynamics of oak—beech stands over the past 50 yrs were analysed. Generalized linear mixed-effects models were fitted to quantify annual mortality rates of oak and beech based on DBH, stand basal area, precipitation and slope. Results: Stand basal area increased, whereas tree density decreased over time. At most sites, the relative importance of oak decreased gradually compared to beech. Mortality increased over time for both oak and beech, but the increase was stronger for oak. Oak and beech mortality decreased with increasing DBH and tended to increase with precipitation. Additionally, oak mortality increased with stand basal area, whereas no such trend was found for beech. Conclusion: Our study indicates that mortality in Central European oak—beech forests is driven by a combination of stand structures (i.e. tree size and stand basal area) and climate. However, the influence of climate on oak mortality is comparably low. Increasing oak mortality with stand basal area is a plausible consequence of its lower relative competitiveness and higher demand for light. Thus, in forests developing towards higher stand basal area, the ecologically important oak is increasingly outcompeted by beech, unless competition is reduced through management or disturbances.

The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called ‘inclusive’ R2, as the square of the structure coefficients times total R2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.

Rapid losses and degradation of natural habitats in the tropics are driving catastrophic declines and extinctions of native biotas, including angiosperms. Determining the ecological and life-history correlates of extinction proneness in tropical plant species may help reveal the mechanisms underlying their responses to habitat disturbance, and assist in the pre-emptive identification of species at risk from extinction. We determined the predictors of extinction proneness in 1884 locally extinct (n = 454) and extant (n = 1430) terrestrial angiosperms (belonging to 43 orders, 133 families, and 689 genera) in the tropical island nation of Singapore (699.4 km²), which has lost 99.6% of its primary lowland evergreen rainforest since 1819. A wide variety of traits such as geographical distribution, pollination system, sexual system, habit, habitat, height, fruit/seed dispersal mechanism, and capacity for vegetative re-sprouting were used in the analysis. Despite controlling for phylogeny (as approximated by family level classification), we found that only a small percentage of the variation in the extinction probability could be explained by these factors. Epiphytic, monoecious, and hermaphroditic species and those restricted to inland forests have higher probabilities of extinction. Species dependent on mammal pollinators also probably have higher extinction probabilities. More comparative studies that use species traits to identify extinction-prone plant species are needed to guide the enormous, but essential task of identifying species most in need of conservation action.

Conservation policies often take for granted the importance of protected areas for supplying ecosystem services. The first edition of the State of Protected and Conserved Areas in Eastern and Southern Africa report contained limited information on ecosystem services, so for the 2nd edition we statistically compared 561 standardized economic values of various types of ecosystem services inside and outside of protected areas. We found that data from local and sub‐national case studies in the Ecosystem Service Valuation Database were biased geographically, highlighting major evidence gaps for most of the region. For well‐studied countries (Botswana, Ethiopia, Kenya, South Africa, Tanzania, Uganda), the value of ecosystem services varied considerably across different types of services but were—on average—three to six times higher outside protected areas. This trend was not universal, however, given that opportunities for recreation and tourism tended to be higher within protected areas. Combined, these findings suggest that conservation authorities across Eastern and Southern Africa (1) prioritize ecosystem service valuation studies; (2) expand the focus of ecosystem service policies to include wider landscapes beyond protected area boundaries; and (3) avoid generic assumptions about ecosystem services by identifying the services that are most compatible with the broader goals of protected areas. This study reports on an analysis of the value of ecosystem services in protected areas in Eastern and Southern Africa. Although data were biased geographically, ecosystem services in countries with sufficient data were generally more valuable outside of protected areas. However, certain types of ecosystem services were more valuable in protected areas, suggesting that more nuanced approaches are needed to identify types of services most compatible with the broader goals of protection.

We developed predictive models of coral reef fish species richness and abundance that account for both broad-scale environmental gradients and fine-scale biotic processes, such as dispersal, and we compared the importance of absolute geographical location (i.e. geographical coordinates) versus relative geographical location (i.e. distance to domain boundaries). Great Barrier Reef, Australia. Four annual surveys of coral reef fishes were combined with a 0.01°-resolution grid of environmental variables including depth, sea surface temperature, salinity and nutrient concentrations. A principal component-based method was developed to select candidate predictors from a large number of correlated variables. Generalized linear mixed-effects models (GLMMs) were used to gauge the respective importance of the different spatial and environmental predictors. An error covariance matrix was included in the models to account for spatial autocorrelation. (1) Relative geographical descriptors, represented by distances to the coast and to the barrier reef, provided the highest-ranked single model of species richness and explained up to 36.8% of its deviance. (2) Accounting for spatial autocorrelation doubled the deviance in abundance explained to 71.9%. Sea surface temperature, salinity and nitrate concentrations were also important predictors of abundance. Spatially explicit predictions of species richness and abundance were robust to variation in the spatial scale considered during model calibration. This study demonstrates that distance-to-domain boundaries (i.e. relative geographical location) can offer an ecologically relevant alternative to geographical coordinates (i.e. absolute geographical location) when predicting biodiversity patterns, providing a proxy for multivariate and complex environmental processes that are often difficult or expensive to estimate.

Background Systematic reviews and meta-analyses of binary outcomes are widespread in all areas of application. The odds ratio, in particular, is by far the most popular effect measure. However, the standard meta-analysis of odds ratios using a random-effects model has a number of potential problems. An attractive alternative approach for the meta-analysis of binary outcomes uses a class of generalized linear mixed models (GLMMs). GLMMs are believed to overcome the problems of the standard random-effects model because they use a correct binomial-normal likelihood. However, this belief is based on theoretical considerations, and no sufficient simulations have assessed the performance of GLMMs in meta-analysis. This gap may be due to the computational complexity of these models and the resulting considerable time requirements. Methods The present study is the first to provide extensive simulations on the performance of four GLMM methods (models with fixed and random study effects and two conditional methods) for meta-analysis of odds ratios in comparison to the standard random effects model. Results In our simulations, the hypergeometric-normal model provided less biased estimation of the heterogeneity variance than the standard random-effects meta-analysis using the restricted maximum likelihood (REML) estimation when the data were sparse, but the REML method performed similarly for the point estimation of the odds ratio, and better for the interval estimation. Conclusions It is difficult to recommend the use of GLMMs in the practice of meta-analysis. The problem of finding uniformly good methods of the meta-analysis for binary outcomes is still open.

Aim: There is global consensus that marine protected areas offer a plethora of benefits to the biodiversity within and around them. Nevertheless, many organisms threatened by human impacts also find shelter in unexpected or informally protected places. For coral reef organisms, refuges can be tourist resorts implementing local environment-friendly bottom-up management strategies. We used the coral reef ecosystem as a model to test whether such practices have positive effects on the biodiversity associated with de facto protected areas. Location: North Ari Atoll, Maldives. Methods: We modelled the effects of the environment and three human management regimes (tourist resorts, uninhabited and local community islands) on the abundance and diversity of echinoderms and commercially important fish species, the per cent cover of reef benthic organisms (corals, calcareous coralline algae, turf and macroalgae) and the proportion of coral disease. We used multivariate techniques to assess the differences between reef components among the management regimes. Results: Reefs varied between the management regimes. A positive \"resort effect\" was found on sessile benthic organisms, with good coral cover and significantly less algae at resort islands. Corals were larger and had fewer diseases in uninhabited islands. Minor \"resort effect\" was detected on motile species represented by commercial fish and echinoderms. Main conclusions: In countries where natural biodiversity strongly sustains the tourist sector and where local populations rely on natural resources, a balance between tourism development, local extraction practices and biodiversity conservation is necessary. The presence of eco-friendly managed resorts, which practices would need to be certified on the long term, is beneficial to protect certain organisms. House reefs around resorts could therefore provide areas adding to existing marine protected areas, while marine protection efforts in local community islands should focus on improving fishing management.

Signal detection theory (SDT; Tanner & Swets in Psychological Review 61:401–409, 1954) is a dominant modeling framework used for evaluating the accuracy of diagnostic systems that seek to distinguish signal from noise in psychology. Although the use of response time data in psychometric models has increased in recent years, the incorporation of response time data into SDT models remains a relatively underexplored approach to distinguishing signal from noise. Functional response time effects are hypothesized in SDT models, based on findings from other related psychometric models with response time data. In this study, an SDT model is extended to incorporate functional response time effects using smooth functions and to include all sources of variability in SDT model parameters across trials, participants, and items in the experimental data. The extended SDT model with smooth functions is formulated as a generalized linear mixed-effects model and implemented in the gamm4R package. The extended model is illustrated using recognition memory data to understand how conversational language is remembered. Accuracy of parameter estimates and the importance of modeling variability in detecting the experimental condition effects and functional response time effects are shown in conditions similar to the empirical data set via a simulation study. In addition, the type 1 error rate of the test for a smooth function of response time is evaluated.

Background Disordered amino acid metabolism is observed in cerebral malaria (CM). This study sought to determine whether abnormal amino acid concentrations were associated with level of consciousness in children recovering from coma. Twenty-one amino acids and coma scores were quantified longitudinally and the data were analysed for associations. Methods In a prospective observational study, 42 children with CM were enrolled. Amino acid levels were measured at entry and at frequent intervals thereafter and consciousness was assessed by Blantyre Coma Scores (BCS). Thirty-six healthy children served as controls for in-country normal amino acid ranges. Logistic regression was employed using a generalized linear mixed-effects model to assess associations between out-of-range amino acid levels and BCS. Results At entry 16/21 amino acid levels were out-of-range. Longitudinal analysis revealed 10/21 out-of-range amino acids were significantly associated with BCS. Elevated phenylalanine levels showed the highest association with low BCS. This finding held when out-of-normal-range data were analysed at each sampling time. Conclusion Longitudinal data is provided for associations between abnormal amino acid levels and recovery from CM. Of 10 amino acids significantly associated with BCS, elevated phenylalanine may be a surrogate for impaired clearance of ether lipid mediators of inflammation and may contribute to CM pathogenesis.

Background Although antenatal care has the potential role to reduce maternal and child morbidity and mortality, utilization of a recommended number of antenatal care visits is still low in Ethiopia. Therefore, this study aimed to assess the determinants of recommended antenatal care visits in Ethiopia. Method Data from the 2019 mini-Ethiopian demographic and health survey (MEDHS) was used for this study. A total of 3916 women who gave birth 5 years preceding the MEDHS were included. A generalized linear mixed-effects (mixed-effects logistic regression) model was used to identify the determinants of recommended antenatal care service utilization. Finally, the adjusted odds ratio with a 95% confidence interval and random effects were reported. Results In the generalized linear mixed-effects model, women with primary education (AOR = 1.55, 95%CI 1.22–2.01), secondary and above education (AOR = 5.12, 95%CI 2.80–8.16), women from the middle (AOR = 1.25, 95%CI 1.01–1.71) and rich wealth index (AOR = 1.54, 95%CI 1.12–2.25), women who were exposed to media (AOR = 1.23,95%CI 1.01–1.57) and who use contraception (AOR = 1.45 95%CI 1.25–2.03), had higher odds of recommended antenatal care service utilization. Conclusion In this study, factors like maternal educational status, media exposure, wealth index and history of contraceptive utilization were significantly associated with recommended ANC visits in Ethiopia. Therefore, encouraging women for contraceptive service utilization, consulting women to be exposed to media and improving women’s wealth status will help to have recommended number of ANC visits by pregnant women in Ethiopia.