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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.

Growing interest in proximate and ultimate causes and consequences of between‐ and within‐individual variation in labile components of the phenotype – such as behaviour or physiology – characterizes current research in evolutionary ecology. The study of individual variation requires tools for quantification and decomposition of phenotypic variation into between‐ and within‐individual components. This is essential as variance components differ in their ecological and evolutionary implications. We provide an overview of how mixed‐effect models can be used to partition variation in, and correlations among, phenotypic attributes into between‐ and within‐individual variance components. Optimal sampling schemes to accurately estimate (with sufficient power) a wide range of repeatabilities and key (co)variance components, such as between‐ and within‐individual correlations, are detailed. Mixed‐effect models enable the usage of unambiguous terminology for patterns of biological variation that currently lack a formal statistical definition (e.g. ‘animal personality’ or ‘behavioural syndromes’), and facilitate cross‐fertilisation between disciplines such as behavioural ecology, ecological physiology and quantitative genetics.

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.

1. Efforts to understand the links between evolutionary and ecological dynamics hinge on our ability to measure and understand how genes influence phenotypes, fitness and population dynamics. Quantitative genetics provides a range of theoretical and empirical tools with which to achieve this when the relatedness between individuals within a population is known. 2. A number of recent studies have used a type of mixed-effects model, known as the animal model, to estimate the genetic component of phenotypic variation using data collected in the field. Here, we provide a practical guide for ecologists interested in exploring the potential to apply this quantitative genetic method in their research. 3. We begin by outlining, in simple terms, key concepts in quantitative genetics and how an animal model estimates relevant quantitative genetic parameters, such as heritabilities or genetic correlations. 4. We then provide three detailed example tutorials, for implementation in a variety of software packages, for some basic applications of the animal model. We discuss several important statistical issues relating to best practice when fitting different kinds of mixed models. 5. We conclude by briefly summarizing more complex applications of the animal model, and by highlighting key pitfalls and dangers for the researcher wanting to begin using quantitative genetic tools to address ecological and evolutionary questions.

1. While various studies have evaluated the habitat requirements for wildlife in fragmented forest landscapes at multiple spatial scales, few have considered whether there is regional variation in the most important factors. This is a conundrum for managers of any species with a broad geographical range: to what extent should studies in one region inform decisions in another? 2. We addressed this question using a case study of the koala, Phascolarctos cinereus, in three biogeographically different fragmented forested landscapes in eastern Australia. Mixed-effect logistic models were applied to predict koala occurrence from explanatory variables captured at four spatial scales: the individual tree, the stand (<1 ha), the patch (1-100 ha) and the landscape (100-1000 ha). 3. We used model averaging to account for model and parameter uncertainty, and tested the cross-regional discrimination ability of the models. 4. We discovered that multiscale models of koala distribution cannot be readily generalized from region to region, and that specific conservation actions for each region, rather than the entire geographical range, are more appropriate. We found a strong justification for adopting a hierarchical landscape approach to koala conservation across its range. However, cross-regional differences in habitat relationships occurred within this hierarchy. Exceptions were landscape context, which showed a consistently strong effect and high rank in all regions, and the presence of individual preferred tree species of the genus Eucalyptus, which showed modest consistency in its interaction with large-diameter trees across the regions. In contrast, the remaining habitat variables, including patch size (a key management factor), showed moderate to strong cross-regional variation attributed to the interaction of edaphic factors, landscape history and contemporary land-use patterns. 5. Synthesis and applications. Adopting a uniform conservation programme over a large geographical area is attractive to policy-makers and conservation planners. However, our study confirms the lack of generality of species distribution models over large areas. Consequently, we argue against adopting a uniform conservation programme for species with a large geographical range.

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.

Normality of random effects is a routine assumption for the linear mixed model, but it may be unrealistic, obscuring important features of among‐individual variation. We relax this assumption by approximating the random effects density by the seminonparameteric (SNP) representation of Gallant and Nychka (1987, Econometrics55, 363–390), which includes normality as a special case and provides flexibility in capturing a broad range of nonnormal behavior, controlled by a user‐chosen tuning parameter. An advantage is that the marginal likelihood may be expressed in closed form, so inference may be carried out using standard optimization techniques. We demonstrate that standard information criteria may be used to choose the tuning parameter and detect departures from normality, and we illustrate the approach via simulation and using longitudinal data from the Framingham study.

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.

Meta‐analysis produces a quantitative synthesis of evidence‐based knowledge, shaping not only research trends but also policies and practices in biology. However, two statistical issues, selective reporting and statistical dependence, can severely distort meta‐analytic parameter estimation and inference. Here, we re‐analyse 448 meta‐analyses to demonstrate a new two‐step procedure to deal with two common challenges in biological meta‐analyses that often occur simultaneously: publication bias and non‐independence. First, we employ bias‐robust weighting schemes under the generalized least square estimator to obtain average effect sizes that are more robust to selective reporting. We then use cluster‐robust variance estimation to account for statistical dependence, reducing bias in estimating standard errors and ensuring valid statistical inference. The first step of our approach demonstrates comparable performance in estimating average effect sizes to the existing publication‐bias adjustment methods in the presence of selective reporting. This equivalence holds across two publication bias selection processes. The second step achieves estimates of standard errors consistent with the multilevel meta‐analytic model, a benchmark method with adequate control of Type I error rates for multiple, statistically dependent effect sizes. Re‐analyses of 448 meta‐analyses show that ignoring these two issues tends to overestimate effect sizes by an average of 110% and underestimate standard errors by 120%. To facilitate implementation, we have developed a website including a step‐by‐step tutorial. Complementing current meta‐analytic workflows with the proposed method as a sensitivity analysis can facilitate a transition to a more robust approach in quantitative evidence synthesis.

Soil properties can buffer forest response to global climate change. However, it is unclear how soil characteristics, water availability and their interactions can affect drought response of trees. The aim of this study was to assess the influence of soil nutrients and physical soil properties on the growth sensitivity of Fagus sylvatica, Quercus spp., Fraxinus excelsior, Abies alba, Picea abies and Pinus sylvestris to drought in Central Europe. Yearly growth data from increment cores were obtained from 538 trees and combined with forest inventory and soil data at 52 sites covering a large gradient of water availability and C/N ratios in soil. Linear mixed‐effects models were used to assess the species‐specific growth responses to climate and soil properties for the period 1957–2006. The growth of the species was further projected across the full range of C/N and water availability observed at 1029 sites where soil and species cover‐abundance data were available. Temperature, water and nutrient availability (C/N) were the most important factors for tree growth. Drought and low nutrient availability significantly reduced the growth of beech, ash, fir and spruce along the gradient. In contrast, the growth of pine and oak was little reduced on poor and dry sites, hence showing their competitive advantage over nutrient‐demanding species under such conditions. The growth of ash and pine was enhanced at sites with high species abundance, whereas an opposite response was found for spruce. No clear relationships between growth and species abundance were found for beech, oak and fir. Synthesis. Our results suggest that assessing tree responses to climate change without considering simultaneously soil properties and climate may be misleading since soil nutrients can influence growth response of trees to drought. A detailed analysis of the influence of the soil characteristics on growth responses of trees is necessary to understand the sensitivity of tree species to global climate change.