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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.

Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. An important source of this uncertainty lies in the dependency of photosynthesis on the maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax). Understanding and making accurate prediction of C fluxes thus requires accurate characterization of these rates and their relationship with plant nutrient status over large geographic scales. Plant nutrient status is indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Correlations between Vcmax and Jmax and leaf nitrogen (N) are typically derived from local to global scales, while correlations with leaf phosphorus (P) and specific leaf area (SLA) have typically been derived at a local scale. Thus, there is no global‐scale relationship between Vcmax and Jmax and P or SLA limiting the ability of global‐scale carbon flux models do not account for P or SLA. We gathered published data from 24 studies to reveal global relationships of Vcmax and Jmax with leaf N, P, and SLA. Vcmax was strongly related to leaf N, and increasing leaf P substantially increased the sensitivity of Vcmax to leaf N. Jmax was strongly related to Vcmax, and neither leaf N, P, or SLA had a substantial impact on the relationship. Although more data are needed to expand the applicability of the relationship, we show leaf P is a globally important determinant of photosynthetic rates. In a model of photosynthesis, we showed that at high leaf N (3 gm−2), increasing leaf P from 0.05 to 0.22 gm−2 nearly doubled assimilation rates. Finally, we show that plants may employ a conservative strategy of Jmax to Vcmax coordination that restricts photoinhibition when carboxylation is limiting at the expense of maximizing photosynthetic rates when light is limiting. Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. To reduce this uncertainty we analysed data collected in the literature from across the globe on the maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) in relation to plant nutrient status indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Vcmax was strongly related to leaf N and increasing leaf P substantially increased the sensitivity of Vcmax to leaf N and in a model of photosynthesis we showed that at high leaf N (3 gm−2) increasing leaf P from 0.05 to 0.22 gm−2 nearly doubled assimilation rates. Finally we show that plants may employ a conservative strategy of Jmax to Vcmax co‐ordination that restricts photoinhibition when carboxylation is limiting at the expense of maximising photosynthetic rates when light is limiting.

Questions: We aim for a better understanding of the different modes of intra- and inter-specific competition in two- and three-species mixed-forests. How can the effect of different modes of competitive interactions be detected and integrated into individual tree growth models? Are species interactions in spruce–fir–beech forests more associated with size-symmetric or size-asymmetric competition? Do competitive interactions between two of these species change from two- to three-species mixtures? Location: Temperate mixed-species forests in Central Europe (Switzerland). Methods: We used data from the Swiss National Forest Inventory to fit basal area increment models at the individual tree level, including the effect of ecological site conditions and indices of size-symmetric and size-asymmetric competition. Interaction terms between species-specific competition indices were used to disentangle significant differences in species interactions from two- to three-species mixtures. Results: The growth of spruce and fir was positively affected by increasing proportions of the other species in spruce–fir mixtures, but negative effects were detected with increasing presence of beech. We found that competitive interactions for spruce and fir were more related to size-symmetric competition, indicating that species interactions might be more associated with competition for below-ground resources. Under constant amounts of stand basal area, the growth of beech clearly benefited from the increasing admixture of spruce and fir. For this species, patterns of size-symmetric and size-asymmetric competitive interactions were similar, indicating that beech is a strong self-competitor for both above-ground and below-ground resources. Only for silver fir and beech, we found significant changes in species interactions from two- to three-species mixtures, but these were not as prominent as the effects due to differences between intra- and inter-specific competition. Conclusions: Species interactions in spruce–fir–beech, or other mixed forests, can be characterized depending on the mode of competition, allowing interpretations of whether they occur mainly above or below ground level. Our outcomes illustrate that species-specific competition indices can be integrated in individual tree growth functions to express the different modes of competition between species, and highlight the importance of considering the symmetry of competition alongside competitive interactions in models aimed at depicting growth in mixed-species forests.

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.

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.

Integrated step‐selection analysis (ISSA) is frequently used to study habitat selection using animal movement data. Methods for incorporating random effects in ISSA have been developed, making it possible to quantify variability among animals in their space‐use patterns. Although it is possible to model variability in both habitat selection and movement parameters, applications to date have focused on the former despite the widely acknowledged and important role that movement plays in determining ecological processes from the individual to ecosystem level. One potential explanation for this omission is the absence of readily available software or examples demonstrating methods for estimating movement parameters in ISSA with random effects. We demonstrated methods for characterizing among‐individual variability in both movement and habitat‐selection parameters using a simulated data set and by fitting two models to an acoustic telemetry data set containing locations of 35 red snapper (Lutjanus campechanus). Movement kernels were assumed to depend on either the type of benthic reef habitat in which the fish was located (model 1) or the distance between the fish's current location and the nearest edge habitat (model 2). In both models, we also quantified habitat selection for different benthic habitat classes and distance to edge habitat, and we allowed for individual variability in movement and habitat‐selection parameters using random effects. The simulation example highlights the benefits of a mixed‐effects specification, namely, we can increase precision when estimating individual‐specific movement parameters by borrowing information across like individuals. In our applied example, we found substantial among‐individual variability in both habitat selection and movement parameters. Nonetheless, most red snapper selected for hardbottom habitat and for locations nearer to edge habitat. They also moved less when in hardbottom habitat. Turn angles were frequently near ± π but were more dispersed when fish were far away from edge habitat. We provide code templates and functions for quantifying variability in movement and habitat‐selection parameters when implementing ISSA with random effects. In doing so, we hope to encourage ecologists conducting ISSA to take full advantage of their ability to model among‐individual variability in both habitat‐selection and movement patterns.

Animal ecologists frequently quantify variance in hierarchically structured traits in wild populations. Importantly, phenotypic plasticity within the period of measurement can modify the trait of interest in response to various unmeasured, temporally or spatially changeable, environmental conditions. Non‐random sampling among units of the random effect (e.g. individuals) regarding the environment at issue may lead to estimates of the variance among (σ̂I2) or within (σ̂W2) such units that conflate several types of processes. This mixing of underlying biology can affect interpretations of the random effect variance. Here, we explore the conditions leading to this situation and assess potential solutions when relevant information is missing. We simulated a trait's phenotypic values that depended on the environmental variable, and individuals that differed in their deviation to the mean population phenotype (random intercepts). We also simulated different types of variation in an environmental variable that was either shared or specific to each individual. We then varied the repeatability in the timing of sampling (RIS2) and analysed simulated datasets using linear mixed‐effect models with different fixed‐ and random‐effect structures. In the presence of unmeasured environmental factors, the estimated among‐individual variance (σ̂I2) contained a larger signature of the current environment as the strength of the temporal autocorrelation and the repeatability in the timing of sampling (RIS2) increased. For low to moderate values of RIS2 (e.g. <60% of the total variance in our simulations) the risk of pre‐study and within‐study effects conflating estimates of variance components was low and could easily be corrected with a model including period or individual‐period combination as random effects. Higher RIS2 led to an increase in conflating effects that were difficult to correct. Our study shows the importance of limiting the variance among individuals in the timing structure of sampling (RIS2). We recommend researchers estimate RIS2 and report it in papers. Finally, RIS2 can be limited by sampling all individuals in the same period, or sensitivity analyses could be conducted by removing extreme sampling dates at the analysis stage to reduce RIS2.

Aim: Habitat fragmentation and loss are two of the most important factors driving current biodiversity decline. Nonetheless, the relationship between biodiversity and landscape patterns appears more complex than generally expected, depending on the species and communities involved. We aim to enrich knowledge concerning the relationship between plant diversity and landscape patterns along linear landscapes, such as Mediterranean coastal dunes. A dedicated buffering method considering multiple nested extents was developed for sampling linear landscapes (e.g. coastal or fluvial), which traditionally present a challenge for standard round or square sampling buffering approaches. Location: Tyrrhenian coast of central Italy. Methods: Based on a database of plant community plots and functional traits from field measurements, for each plot we calculated taxonomic (TD) and functional (FD) diversity, which was further decomposed in functional evenness (FDeven) and mean trait dispersion (FDdisp). Relying on a land-cover map, we computed a set of landscape metrics describing habitat loss, fragmentation and direct human disturbance at multiple extents around each plot. Diversity measures (TD, FD, FDeven and FDdisp) were then related to the landscape metrics at different scales via linear mixed-effect models. Results: Overall, the relationship between plant species diversity and landscape patterns was weak. We observed different responses of TD, FD, FDeven and FDdisp, which only emerged at fine-medium scales. TD decreased with habitat loss and disturbance, while FD only with disturbance. FDeven decreased in more fragmented areas, while FDdisp was not affected by the selected landscape parameters. Main conclusions: Like other transitional areas, coastal strand and dune ecosystems exhibit steep gradients in biotic and environmental factors, are dynamic in location, and could be among the earliest to be affected by environmental drivers. However, the response of Mediterranean coastal dune plant diversity to habitat loss and fragmentation is weak. For these reasons, we propose that these plant communities are adapted to the ever-changing nature of the coastal environment and consequently to changes in landscape pattern.

Mixed-effects models for repeated measures and longitudinal data include random coefficients that are unique to the individual, and thus permit subject-specific growth trajectories, as well as direct study of how the coefficients of a growth function vary as a function of covariates. Although applications of these models often assume homogeneity of the within-subject residual variance that characterizes within-person variation after accounting for systematic change and the variances of the random coefficients of a growth model that quantify individual differences in aspects of change, alternative covariance structures can be considered. These include allowing for serial correlations between the within-subject residuals to account for dependencies in data that remain after fitting a particular growth model or specifying the within-subject residual variance to be a function of covariates or a random subject effect to address between-subject heterogeneity due to unmeasured influences. Further, the variances of the random coefficients can be functions of covariates to relax the assumption that these variances are constant across subjects and to allow for the study of determinants of these sources of variation. In this paper, we consider combinations of these structures that permit flexibility in how mixed-effects models are specified to understand within- and between-subject variation in repeated measures and longitudinal data. Data from three learning studies are analyzed using these different specifications of mixed-effects models.