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Aim: Higher-elevation areas on islands and continental mountains tend to be separated by longer distances, predicting higher endemism at higher elevations; our study is the first to test the generality of the predicted pattern. We also compare it empirically with contrasting expectations from hypotheses invoking higher speciation with area, temperature and species richness. Location: Thirty-two insular and 18 continental elevational gradients from around the world. Methods: We compiled entire floras with elevation-specific occurrence information, and calculated the proportion of native species that are endemic ('percent endemism') in 100-m bands, for each of the 50 elevational gradients. Using generalized linear models, we tested the relationships between percent endemism and elevation, isolation, temperature, area and species richness. Results: Percent endemism consistently increased monotonically with elevation, globally. This was independent of richness—elevation relationships, which had varying shapes but decreased with elevation at high elevations. The endemism—elevation relationships were consistent with isolation-related predictions, but inconsistent with hypotheses related to area, richness and temperature. Main conclusions: Higher per-species speciation rates caused by increasing isolation with elevation are the most plausible and parsimonious explanation for the globally consistent pattern of higher endemism at higher elevations that we identify. We suggest that topography-driven isolation increases speciation rates in mountainous areas, across all elevations and increasingly towards the equator. If so, it represents a mechanism that may contribute to generating latitudinal diversity gradients in a way that is consistent with both present-day and palaeontological evidence.

The objective of this study is to present long-term forecasts of the accumulated number of confirmed cases and deaths from COVID-19 per million inhabitants from the first confirmed case to day 350 in each South American country, using the logistic growth model with three parameters. Comparing the forecasts of the number of confirmed cases and deaths, it was observed that Paraguay, Peru and Argentina will take longer to significantly reduce the number of confirmed cases and Argentina in the number of deaths. [...]Venezuela and Uruguay present the number of confirmed cases and deaths accumulated almost constantly, that is, there will be no significant increases during the following months. Keywords: logistic growth model; COVID-19 SARS-COV2, Nonlinear Mixed Effects Models. 1.

Unique combinations of geographic and environmental conditions make quantifying the importance of factors that influence forest productivity difficult. I aimed to model the height growth of dominant Nothofagus alpina trees in temperate forests of Chile, as a proxy for forest productivity, by building a dynamic model that accounts for topography, habitat type, and climate conditions. Using stem analysis data of 169 dominant trees sampled throughout south-central Chile (35°50′ and 41°30′ S), I estimated growth model parameters using a nonlinear mixed-effects framework that takes into account the hierarchical structure of the data. Based on the proposed model, I used a system-dynamics approach to analyze growth rates as a function of topographic, habitat type, and climatic variability. I found that the interaction between aspect, slope, and elevation, as well as the effect of habitat type, play an essential role in determining tree height growth rates of N. alpina. Furthermore, the precipitation in the warmest quarter, precipitation seasonality, and annual mean temperature are critical climatic drivers of forest productivity. Given a forecasted climate condition for the region by 2100, where precipitation seasonality and mean annual temperature increase by 10% and 1°C, respectively, and precipitation in the warmest quarter decreases by 10 mm, I predict a reduction of 1.4 m in height growth of 100-yr-old dominant trees. This study shows that the sensitivity of N. alpina-dominated forests to precipitation and temperature patterns could lead to a reduction of tree height growth rates as a result of climate change, suggesting a decrease in carbon sequestration too. By implementing a system dynamics approach, I provide a new perspective on climate-productivity relationships, bettering the quantitative understanding of forest ecosystem dynamics under climate change. The results highlight that while temperature rising might favor forest growth, the decreasing in both amount and distribution within a year of precipitation can be even more critical to reduce forest productivity.

Biological data are often intrinsically hierarchical (e.g., species from different genera, plants within different mountain regions), which made mixed‐effects models a common analysis tool in ecology and evolution because they can account for the non‐independence. Many questions around their practical applications are solved but one is still debated: Should we treat a grouping variable with a low number of levels as a random or fixed effect? In such situations, the variance estimate of the random effect can be imprecise, but it is unknown if this affects statistical power and type I error rates of the fixed effects of interest. Here, we analyzed the consequences of treating a grouping variable with 2–8 levels as fixed or random effect in correctly specified and alternative models (under‐ or overparametrized models). We calculated type I error rates and statistical power for all‐model specifications and quantified the influences of study design on these quantities. We found no influence of model choice on type I error rate and power on the population‐level effect (slope) for random intercept‐only models. However, with varying intercepts and slopes in the data‐generating process, using a random slope and intercept model, and switching to a fixed‐effects model, in case of a singular fit, avoids overconfidence in the results. Additionally, the number and difference between levels strongly influences power and type I error. We conclude that inferring the correct random‐effect structure is of great importance to obtain correct type I error rates. We encourage to start with a mixed‐effects model independent of the number of levels in the grouping variable and switch to a fixed‐effects model only in case of a singular fit. With these recommendations, we allow for more informative choices about study design and data analysis and make ecological inference with mixed‐effects models more robust for small number of levels. Many questions around the practical applications of mixed‐effects models are solved but one is still debated: Should we treat a grouping variable with a low number of levels as a random or fixed‐effect? Here, we analyzed the consequences of treating a grouping variable with 2‐8 levels as fixed‐ or random‐effect in correctly specified and alternative models (under‐ or overparametrized models). We found no influence of model choice on type I error rate and power on the population‐level effect (slope) for random intercept only models, but with varying intercepts and slopes in the data‐generating process, using a random slope and intercept model, and switching to a fixed‐effects model, in case of a singular fit, avoids overconfidence in the results.

Multilevel models are effective marketing analytic tools that can test for consumer differences in longitudinal data. A two-part multilevel model is a special case of a multilevel model developed for semi-continuous data, such as data that include a combination of zeros and continuous values. For repeated measures of media use data, a two-part multilevel model informs market research about consumer-specific likeliness to use media, level of use across time, and variation in use over time. These models are typically estimated using maximum likelihood. There are, however, tremendous advantages to using a Bayesian framework, including the ease at which the analyst can take into account information learned from previous investigations. This paper develops a Bayesian approach to estimating a two-part multilevel model and illustrates its use by applying the model to daily diary measures of television use in a large US sample.

Ecological restoration aims at recovering biodiversity in degraded ecosystems, and it is commonly assessed via species richness. However, it is unclear whether increasing species richness in a site also recovers its functional diversity (FD), which has been shown to be a better representation of ecosystem functioning. We conducted a quantitative synthesis of 30 restoration projects and tested whether restoration improves FD. We compared actively and passively restored sites with degraded and reference sites with respect to four key measures of FD (functional richness, evenness, dispersion, and turnover) and two measures of species diversity (richness and evenness). We separately analyzed longitudinal studies (which monitor degraded, reference, and restored sites through time) and space-for-time substitutions (which compare at one point in time degraded and reference sites with restored sites of different ages). Space-for-time studies suggested that species diversity and FD improved over time. However, replicated longitudinal data showed no sustained benefits of active or passive restoration for FD measures, relative to degraded sites. This could suggest that the positive results in space-for-time designs may have been unreliable, but the relatively short duration of longitudinal studies suggests a need for longer-term longitudinal research to robustly demonstrate the absence of any effect. These differences across study designs may explain the variable results found in recent studies directly measuring the response of FD to restoration. We recommend that future assessments of ecological community dynamics include control sites in monitoring, to ensure that the consequences of treatments, including but not limited to restoration, are correctly partitioned from unassisted temporal changes.

Biological data often violate the assumption of constant variance, yet such heteroscedasticity can reflect meaningful biological processes such as plasticity, canalization or stress responses. Despite this, most models treat variance as statistical noise. Here, we reintroduce location–scale regression as a general framework that jointly models the mean (location) and variance (scale) components of a response. We describe three hierarchical extensions: (1) fixed‐effects, (2) mixed‐effects and (3) double‐hierarchical models, which allow researchers to formally test variance structures alongside mean effects, enhancing biological interpretation. This framework is highly flexible and can extend beyond Gaussian assumptions to accommodate real‐world data. The framework accommodates over‐dispersed, under‐dispersed and zero‐inflated count data through the use of negative binomial and Conway–Maxwell–Poisson distributions, and bounded proportion data through beta‐binomial and beta regressions. Submodels can also be incorporated to account for structural zeros and ones when boundary outcomes are common. These extensions allow researchers to capture ecological processes such as presence–absence, success rates and bounded response rates. Using worked examples from published evolutionary and behavioural ecological studies, we illustrate how location–scale models can uncover biologically meaningful variance patterns that are overlooked in models focused solely on means. For instance, we show how food supplementation, hatching order and predation risk influence not only average trait values but also their variability. Each example corresponds to one of the model types and is implemented using widely used R packages such as glmmTMB and brms. All examples are accompanied by a freely accessible, step‐by‐step online tutorial, thereby lowering technical barriers and fostering broader adoption of location–scale modelling in ecological and evolutionary research. Finally, we propose a practical workflow for model selection and diagnostics and highlight recent extensions of the framework. These include multi‐response models, meta‐analytic models, phylogenetic comparative models and models including shape parameters such as skewness. Treating variance as a biologically informative response opens new avenues for us to explore the evolutionary, ecological and environmental processes that shape biological systems across diverse contexts.

In the present study, we developed and validated a fast, simple, and sensitive quantitative method for the simultaneous determination of eleven nucleosides and deoxynucleosides from urine samples. The analyses were performed with the use of liquid chromatography coupled with triple quadrupole mass spectrometry. The sample pretreatment procedure was limited to centrifugation, vortex mixing of urine samples with a methanol/water solution (1:1, v/v ), evaporation and dissolution steps. The analysis lasted 20 min and was performed in dynamic multiple reaction monitoring mode (dMRM) in positive polarity. Process validation was conducted to determine the linearity, precision, accuracy, limit of quantification, stability, recovery and matrix effect. All validation procedures were carried out in accordance with current FDA and EMA regulations. The validated method was applied for the analysis of 133 urine samples derived from bladder cancer patients before tumor resection and 24 h, 2 weeks, and 3, 6, 9, and 12 months after the surgery. The obtained data sets were analyzed using a linear mixed-effect model. The analysis revealed that concentration level of 2-methylthioadenosine was decreased, while for inosine, it was increased 24 h after tumor resection in comparison to the preoperative state. The presented quantitative longitudinal study of urine nucleosides and deoxynucleosides before and up to 12 months after bladder tumor resection brings additional prospective insight into the metabolite excretion pattern in bladder cancer disease. Moreover, incurred sample reanalysis was performed proving the robustness and repeatability of the developed targeted method. Graphical abstract

Aphids represent a significant challenge to food production. The Rothamsted Insect Survey (RIS) runs a network of 12·2‐m suction‐traps throughout the year to collect migrating aphids. In 2014, the RIS celebrated its 50th anniversary. This paper marks that achievement with an extensive spatiotemporal analysis and the provision of the first British annotated checklist of aphids since 1964. Our main aim was to elucidate mechanisms that advance aphid phenology under climate change and explain these using life‐history traits. We then highlight emerging pests using accumulation patterns. Linear and nonlinear mixed‐effect models estimated the average rate of change per annum and effects of climate on annual counts, first and last flights and length of flight season since 1965. Two climate drivers were used: the accumulated day degrees above 16 °C (ADD16) indicated the potential for migration during the aphid season; the North Atlantic Oscillation (NAO) signalled the severity of the winter before migration took place. All 55 species studied had earlier first flight trends at rate of β = −0·611 ± SE 0·015 days year⁻¹. Of these species, 49% had earlier last flights, but the average species effect appeared relatively stationary (β = −0·010 ± SE 0·022 days year⁻¹). Most species (85%) showed increasing duration of their flight season (β = 0·336 ± SE 0·026 days year⁻¹), even though only 54% increased their log annual count (β = 0·002 ± SE <0·001 year⁻¹). The ADD16 and NAO were shown to drive patterns in aphid phenology in a spatiotemporal context. Early in the year when the first aphids were migrating, the effect of the winter NAO was highly significant. Further into the year, ADD16 was a strong predictor. Latitude had a near linear effect on first flights, whereas longitude produced a generally less‐clear effect on all responses. Aphids that are anholocyclic (permanently parthenogenetic) or are monoecious (non‐host‐alternating) were advancing their phenology faster than those that were not. Climate drives phenology and traits help explain how this takes place biologically. Phenology and trait ecology are critical to understanding the threat posed by emerging pests such as Myzus persicae nicotianae and Aphis fabae cirsiiacanthoidis, as revealed by the species accumulation analysis.

Individual trees, not forests, respond to climate. Such an individual‐scale approach has seldom been used to retrospectively track the radial growth responses of trees to climate in dendrochronology. The aim of this study was to adopt this individual view to retrospectively assess tree sensitivity to climate warming, and to evaluate and compare the potential drivers of tree growth responses to climate acting at species, site and individual scales. Following a dendroecological framework, we sampled a network of 29 Pinus uncinata forests in NE Spain and obtained tree‐ring widths series from 642 trees. Individual features as northness, elevation, slope, basal area, sapwood area, tree height and tree age were used to evaluate the potential drivers of tree growth responses to climate. The analysed data set includes diverse ecological and biogeographical conditions. The tree growth responses to climate were assessed by relating growth indices to climatic variables using linear‐mixed effects models. Maximum November temperatures during the year prior to tree‐ring formation enhanced P. uncinata growth mainly in mid‐elevation sites, whereas at higher elevations growth was more dependent on the positive effect of warmer minimum May temperatures during the year of tree‐ring formation. Current June precipitation was the positive main climatic driver of growth in sites prone to water deficit such as the southernmost limit of the species distribution area or very steep sites. Elevation was the main factor controlling how much growth variability is explained by climate at the site and tree scales. Climate warming was more intense during the early 20th century, when the importance of elevation as an indirect modulator of growth declined as compared with the late 20th century. Synthesis. The individual‐scale approach taken in this study allowed detecting that trees growing at southern and low‐elevation sites were the most negatively affected by warm and dry summer conditions. Our results emphasize that both (i) an individual‐scale approach to quantify tree growth responses to climate and (ii) a detailed evaluation of the potential biotic and abiotic drivers of those individual responses are necessary to understand climate sensitivity of trees.