1. Tree height is a key variable for estimating tree biomass and investigating tree life history, but it is difficult to measure in forests with tall, dense canopies and wide crowns. The traditional method, which we refer to as the “tangent method,” involves measuring horizontal distance to the tree and angles from horizontal to the top and base of the tree, while standing at a distance of perhaps one tree height or greater. Laser rangefinders enable an alternative method, which we refer to as the “sine method”; it involves measuring the distances to the top and base of the tree, and the angles from horizontal to these, and can be done from under the tree or from some distance away.
2. We quantified systematic and random errors of these two methods as applied by five technicians to a size-stratified sample of 74 trees between 5.7 and 39.2 m tall in a Neotropical moist forest in Panama. We measured actual heights using towers adjacent to these trees.
3. The tangent method produced unbiased height estimates, but random error was high, and in 6 of the 370 measurements heights were overestimated by more than 100%.
4. The sine method was faster to learn, displayed less variation in heights among technicians, and had lower random error, but resulted in systematic underestimation by 20% on average.
5. We recommend the sine method for most applications in tropical forests. However, its underestimation, which is likely to vary with forest and instrument type, must be corrected if actual heights are needed.

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1. Recent technical and methodological advances have led to a dramatic increase in the use of spectrometry to quantify reflectance properties of biological materials, as well as models to determine how these colours are perceived by animals, providing important insights into ecological and evolutionary aspects of animal visual communication.
2. Despite this growing interest, a unified cross-platform framework for analyzing and visualizing spectral data has not been available. We introduce pavo, an R package that facilitates the organization, visualization, and analysis of spectral data in a cohesive framework. pavo is highly flexible, allowing users to (a) organize and manipulate data from a variety of sources, (b) visualize data using R's state-of-the-art graphics capabilities, and (c) analyze data using spectral curve shape properties and visual system modeling for a broad range of taxa.
3. In this paper, we present a summary of the functions implemented in pavo and how they integrate in a workflow to explore and analyze spectral data. We also present an exact solution for the calculation of colour volume overlap in colourspace, thus expanding previously published methodologies.
4. As an example of pavo's capabilities, we compare the colour patterns of three African Glossy Starling species, two of which have diverged very recently. We demonstrate how both colour vision models and direct spectral measurement analysis can be used to describe colour attributes and differences between these species. Different approaches to visual models and several plotting capabilities exemplify the package's versatility and streamlined workflow.
5. pavo provides a cohesive environment for handling spectral data and addressing complex sensory ecology questions, while integrating with R's modular core for a broader and comprehensive analytical framework, automated management of spectral data, and reproducible workflows for colour analysis.

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1. Species richness, the number of species in a defined area, is the most frequently used biodiversity measure. Despite its intuitive appeal and conceptual simplicity, species richness is often difficult to quantify, even in well-surveyed areas, because of sampling limitations such as survey effort and species detection probability. Nonparametric estimators have generally performed better than other options, but no particular estimator has consistently performed best across variation in assemblage and survey parameters.
2. In order to evaluate estimator performances, we developed the program SimAssem. SimAssem can: 1) simulate assemblages and surveys with user-specified parameters, 2) process existing species encounter history files, 3) generate species richness estimates not available in other programs, and 4) format encounter history data for several other programs.
3. SimAssem can help elucidate relationships between assemblage and survey parameters and the performance of species richness estimators, thereby increasing our understanding of estimator sensitivity, improving estimator development, and defining the bounds for appropriate application.

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1. Despite the fact that pixels (i.e. picture elements) make up the basic sampling units of maps, we are aware of no software package or tool that allows users to model changes that may occur at such fine spatial resolutions over broad geographic extents.
2. Curve Fit is an extension to the application ArcMap that allows users to conduct linear or nonlinear regression analysis on the range of values found within input raster datasets (geo-referenced images), independently for each pixel.
3. Outputs consist of raster surfaces of regression model parameter estimates, standard errors, goodness of fit estimates, and multi-model inference measures.
4. Curve fit outputs characterize continuous spatial or temporal change across a series of raster datasets.

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1. We present a new R package, diveRsity, for the calculation of various diversity statistics, including common diversity partitioning statistics (,) and population differentiation statistics (,,test for population heterogeneity), among others. The package calculates these estimators along with their respective bootstrapped confidence intervals for loci, sample population pairwise and global levels. Various plotting tools are also provided for a visual evaluation of estimated values, allowing users to critically assess the validity and significance of statistical tests from a biological perspective.
2. diveRsity has a set of unique features, which facilitate the use of an informed framework for assessing the validity of the use of traditional F-statistics for the inference of demography, with reference to specific marker types, particularly focusing on highly polymorphic microsatellite loci. However, the package can be readily used for other codominant marker types (e.g. allozymes, SNPs).
3. A detailed example of usage and descriptions of package capabilities are provided. The example demonstrates useful strategies for the exploration of data and interpretation of results generated by diveRsity. Additional on-line resources for the package are also described, including a GUI web app version intended for those with more limited experience using R for statistical analysis.

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1. Modern phylogenetic comparative biology uses data from the relationships among species (phylogeny) combined with comparative information for phenotypic traits to draw model-based statistical inferences about the evolutionary past. Recent years have seen phylogeny methods for evolutionary inference become central in the study of organic evolution.

2. Here, I present two different graphical methods for visualizing phenotypic evolution on the tree. Method 1 is a new approach for plotting the posterior density of stochastically mapped character histories for a binary (two-state) phenotypic trait on a phylogeny. Method 2 is a closely related technique that uses ancestral character estimation to visualize historical character states for a continuous trait along the branches of a tree.

3. One shortcoming of Method 2 is that by mapping the point estimates of ancestral states along the branches of the tree we have effectively ignored the uncertainty associated with ancestral character estimation of continuous traits. To alleviate this issue, I propose a new method for visualizing ancestral state uncertainty using a type of projection of the tree into morphospace called a ‘traitgram.’

4. All of these approaches should prove useful in summarizing complex comparative inferences about ancestral character reconstruction. They are implemented in the freely available and open-source R phylogenetics package ‘phytools.’

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1. We describe an open source R package, marked, for analysis of mark-recapture data to estimate survival and animal abundance.
2. Currently, marked is capable of fitting Cormack-Jolly-Seber (CJS) and Jolly-Seber models with maximum likelihood estimation (MLE) and CJS models with Bayesian Markov Chain Monte Carlo methods. The CJS models can be fitted with MLE using optimization code in R or with Automatic Differentiation Model Builder. The latter allows incorporation of random effects.
3. Some package features include: (i) individual-specific time intervals between sampling occasions, (ii) generation of optimization starting values from generalized linear model approximations, and (iii) prediction of demographic parameters associated with unique combinations of individual and time-specific covariates.
4. We demonstrate marked with a commonly analyzed European dipper (Cinclus cinclus) dataset.
5. The package will be most useful to ecologists with large mark-recapture datasets and many individual covariates.

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1. Ecological communities are composed of populations connected in tangled networks of ecological interactions. Therefore, the extinction of a species can reverberate through the network and cause other (possibly distantly connected) species to go extinct as well. The study of these secondary extinctions is a fertile area of research in ecological network theory.
2. However, to facilitate practical applications, several improvements to the current analytical approaches are needed. In particular, we need to consider that i) species have different “a priori” probabilities of extinction, ii) disturbances can simultaneously affect several species, and iii) extinction risk of consumers likely grows with resource loss. All these points can be included in dynamical models, which are however difficult to parameterize.
3. Here we advance the study of secondary extinctions with Bayesian Networks. We show how this approach can account for different extinction responses using binary – where each resource has the same importance – and quantitative data – where resources are weighted by their importance. We simulate ecological networks using a popular dynamical model (the Allometric Trophic Network model) and use it to test our method.
4. We find that the Bayesian Network model captures the majority of the secondary extinctions produced by the dynamical model, and that consumers’ responses to species loss are best modeled using a nonlinear sigmoid function. We also show that an approach based exclusively on food web structure loses power when species at higher trophic levels are preferentially lost. Because the loss of apex predators is unfortunately widespread, the results highlight a serious limitation of studies on network robustness.

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1. The information in animal colour patterns plays a key role in many ecological interactions; quanitification would help us to study them, but this is problematic. Comparing patterns using human judgement is subjective and inconsistent. Traditional shape analysis is unsuitable as patterns do not usually contain conserved landmarks. Alternative statistical approaches also have weaknesses, particularly as they are generally based on summary measures that discard most or all of the spatial information in a pattern.
2. We present a method for quantifying the similarity of a pair of patterns based on the distance transform of a binary image. The method compares the whole pattern, pixel by pixel, while being robust to small spatial variations among images.
3. We demonstrate the utility of the distance transform method using three ecological examples. We generate a measure of mimetic accuracy between hoverflies (Diptera: Syrphidae) and wasps (Hymenoptera) based on abdominal pattern, and show that this correlates strongly with the perception of a model predator (humans). We calculate similarity values within a group of mimetic butterflies and compare this with proposed pairings of Müllerian comimics. Finally, we characterise variation in clypeal badges of a paper wasp (Polistes dominula) and compare this with previous measures of variation.
4. While our results generally support the findings of existing studies that have used simpler ad hoc methods for measuring differences among patterns, our method is able to detect more subtle variation and hence reveal previously overlooked trends.

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1. When studying senescence in wildlife populations, we are often limited by the sparseness of the available information on the ages of the individuals under study. Additionally, heterogeneity between individuals can be substantial. Ignoring this heterogeneity can lead to biased estimates of the population parameters of interest and can mask senescence.
2. This article demonstrates the use of a recently developed capture–recapture model for extracting age-dependent estimates of survival probabilities for individuals of unknown age and extends the model by allowing for heterogeneity in survival and capture probabilities using finite mixtures.
3. Using simulation, we show that the estimates of age-dependent survival probabilities when age is unknown can be biased when heterogeneity in capture probabilities is not modelled, in contrast to the case of time-dependent survival probabilities when the estimates are robust to similar violations of model assumptions.
4. The methods are demonstrated using a long-term data set of female brushtail possums (Trichosurus vulpecula Kerr) for which age-specific models for survival probabilities indicating senescence are strongly favoured. We found no evidence of heterogeneity in survival but strong evidence of heterogeneity in capture probabilities.
5. These models have a wide range of applications for estimating age dependence in survival when the age is unknown as they can be applied to any capture–recapture data set, as long as it is collected over a period which is longer, and preferably considerably so, than the life span of the species studied.

1. Autonomous acoustic recorders are widely available and can provide a highly efficient method of species monitoring, especially when coupled with software to automate data processing. However, the adoption of these techniques is restricted by a lack of direct comparisons with existing manual field surveys.
2. We assessed the performance of autonomous methods by comparing manual and automated examination of acoustic recordings with a field-listening survey, using commercially available autonomous recorders and custom call detection and classification software. We compared the detection capability, time requirements, areal coverage and weather condition bias of these three methods using an established call monitoring programme for a nocturnal bird, the little spotted kiwi (Apteryx owenii).
3. The autonomous recorder methods had very high precision (>98%) and required <3% of the time needed for the field survey. They were less sensitive, with visual spectrogram inspection recovering 80% of the total calls detected and automated call detection 40%, although this recall increased with signal strength. The areal coverage of the spectrogram inspection and automatic detection methods were 85% and 42% of the field survey. The methods using autonomous recorders were more adversely affected by wind and did not show a positive association between ground moisture and call rates that was apparent from the field counts. However, all methods produced the same results for the most important conservation information from the survey: the annual change in calling activity.
4. Autonomous monitoring techniques incur different biases to manual surveys and so can yield different ecological conclusions if sampling is not adjusted accordingly. Nevertheless, the sensitivity, robustness and high accuracy of automated acoustic methods demonstrate that they offer a suitable and extremely efficient alternative to field observer point counts for species monitoring.

1. Ecologists increasingly wish to use phylogenies, but are hampered by the technical challenge of phylogeny estimation.
2. We present phyloGenerator, an open-source, stand-alone Python program, that makes use of pre-existing sequence data and taxonomic information to largely automate the estimation of phylogenies.
3. phyloGenerator allows nonspecialists to quickly and easily produce robust, repeatable, and defensible phylogenies without requiring an extensive knowledge of phylogenetics. Experienced phylogeneticists may also find it useful as a tool to conduct exploratory analyses.
4. phyloGenerator performs a number of ‘sanity checks’ on users' output, but users should still check their outputs carefully; we give some advice on how to do so.
5. By linking a number of tools in a common framework, phyloGenerator is a step towards an open, reproducible phylogenetic workflow.
6. Bundled downloads for Windows and Mac OSX, along with the source code and an install script for Linux, can be found at http://willpearse.github.io/phyloGenerator (note the capital ‘G’).

1. The recent expansion of continuous-resighting telemetry methods (e.g. acoustic receivers, PIT tag antennae) has created a class of ecological data not well suited for traditional mark–recapture statistics. Estimating survival when continuous recapture data is available ensues a practical problem, because classical capture–recapture models were derived under a discrete sampling scheme that assumes sampling events are instantaneous with respect to the interval between events.
2. To investigate the use of continuous data in survival analysis, we conducted a model structure adequacy simulation that tested the Cormack–Jolly–Seber (CJS) and Barker joint data survival estimation models, which mainly differ through the Barker's inclusion of secondary period information. We simulated a population in which survival and detection occurred as a near continuous (daily) process and collapsed detection information into monthly sampling bins for survival estimation.
3. While both models performed well when survival was time-independent, the CJS was substantially biased for low survival values and time-dependent conditions. Additionally, unlike the CJS, the Barker model consistently performed well over multiple sample sizes (number of marked individuals). However, the high number of parameters in the Barker model led to convergence difficulties, resulting in a need for an alternative optimization method (simulated annealing).
4. We recommend the use of the Barker model when using continuous data for survival analysis, because it outperformed the CJS over a biologically reasonable range of potential parameter values. However, the practical difficulty of implementing the Barker model combined with its shortcomings during two simulations leaves room for the specification of novel statistical methods tailored specifically for continuous mark–resighting data.

1. The R package nupoint provides tools for estimating animal density from point transect surveys in which the conventional point transect assumption of uniform animal distribution in the vicinity of the point is violated.
2. It includes tools for plotting, model selection, goodness-of-fit testing and simulation.
3. This paper describes the main features of the package and illustrates its use by application to two different kinds of survey dataset.

1. Environmental factors, such as wind, may have a strong influence on the detection probability and detection rate of acoustic telemetry tags. The effect of environmental factors may obscure biological effects and distort the interpretation of acoustic telemetry data.
2. This study was undertaken with fish internally tagged with acoustic transmitters containing depth sensors and monitored by an array of automatic receivers. The influence of environmental factors on the hourly detection rate was evaluated using environmental data from a nearby climate station. The signal detection probability was modelled within the framework of general theory of sound propagation in water.
3. Wind was found to have the strongest influence on the detection rate. Transmitter depth range and rain also contributed significantly to the variation in detection rate.
4. By modelling the attenuation coefficient as a function of wind speed, we show that the probability of detecting a free-swimming acoustically tagged animal can be successfully modelled using general sound propagation theory.
5. The approach of modelling detection probability as a function of the attenuation coefficient offers a wide applicability, as it implies a direct link between detection probability and physical characteristics of the water at the study site. Correcting for varying detection probability is in many cases extremely important to do, since rhythms in biological/behavioural factors are often confounded with environmental variables that influence detection probability (e.g. sea breeze, tide).

1. The doubly labelled water method (DLW) is widely used to measure field metabolic rate (FMR), but it has some limitations. Here, we validate an innovative technique for measuring FMR by comparing the turnover of isotopic rubidium (⁸⁶Rb k_b) with DLW depletion and the rate of CO₂ production () measured by flow-through respirometry (FTR) for two dunnart species (Marsupialia: Dasyuridae), Sminthopsis macroura (17 g) and Sminthopsis ooldea (10 g).
2. The rate of metabolism as assessed by (FTR) and ⁸⁶Rb k_b was significantly correlated for both species (S. macroura, r² = 0·81, P = 1·19 × 10⁻⁵; S. ooldea, r² = 0·63, P = 3·84 × 10⁻⁴), as was from FTR and DLW for S. macroura (r² = 0·43, P = 0·039), but not for S. ooldea (r² = 0·29, P = 0·168). There was no relationship between from DLW and ⁸⁶Rb k_b for either species (S. macroura r² = 0·22, P = 0·169; S. ooldea r² = 0·21, P = 0·253). We conclude that ⁸⁶Rb k_b provided useful estimates of metabolic rate for dunnarts.
3. Meta-analysis provided different linear relationships between and ⁸⁶Rb k_b for endotherms and ectotherms, suggesting different proportionalities between metabolic rate and ⁸⁶Rb k_b for different taxa. Understanding the mechanistic basis for this correlation might provide useful insights into the cause of these taxonomic differences in the proportionality. At present, it is essential that the relationship between metabolic rate and ⁸⁶Rb k_b be validated for each taxon of interest.
4. The advantages of the ⁸⁶Rb technique over DLW include lower equipment requirements and technical expertise, and the longer time span over which measurements can be made. The ⁸⁶Rb method might be particularly useful for estimating FMR of groups for which the assumptions of the DLW technique are compromised (e.g. amphibians, diving species and fossorial species), and groups that are practically challenging for DLW studies (e.g. insects).

1. The use of environmental DNA (eDNA) to detect species in aquatic environments such as ponds and streams is a powerful new technique with many benefits. However, species detection in eDNA-based surveys is likely to be imperfect, which can lead to underestimation of the distribution of a species.
2. Site occupancy models account for imperfect detection and can be used to estimate the proportion of sites where a species occurs from presence/absence survey data, making them ideal for the analysis of eDNA-based surveys. Imperfect detection can result from failure to detect the species during field work (e.g. by water samples) or during laboratory analysis (e.g. by PCR).
3. To demonstrate the utility of site occupancy models for eDNA surveys, we reanalysed a data set estimating the occurrence of the amphibian chytrid fungus Batrachochytrium dendrobatidis using eDNA. Our reanalysis showed that the previous estimation of species occurrence was low by 5–10%. Detection probability was best explained by an index of the number of hosts (frogs) in ponds.
4. Per-visit availability probability in water samples was estimated at 0·45 (95% CRI 0·32, 0·58) and per-PCR detection probability at 0·85 (95% CRI 0·74, 0·94), and six water samples from a pond were necessary for a cumulative detection probability >95%. A simulation study showed that when using site occupancy analysis, researchers need many fewer samples to reliably estimate presence and absence of species than without use of site occupancy modelling.
5. Our analyses demonstrate the benefits of site occupancy models as a simple and powerful tool to estimate detection and site occupancy (species prevalence) probabilities despite imperfect detection. As species detection from eDNA becomes more common, adoption of appropriate statistical methods, such as site occupancy models, will become crucial to ensure that reliable inferences are made from eDNA-based surveys.

1. Approaches for efficient statistical estimation of large phylogenies are now available (Bioinformatics, 2006, 22, 2688), and yet we lack adequate tools for synthesizing information from previous analyses into large timetrees. Here, we present a cross-platform r tool that integrates with tree of life efforts by mapping divergence times from an existing timetree (a ‘reference’) to another uncalibrated phylogeny (a ‘target’) that samples from the same lineage. Leveraging existing methods for rate-smoothing phylograms, this tool enables the rapid generation of very large timetrees where direct estimation of the timing of lineage diversification is either impracticable or impossible.
2. The primary output of the tool is to return divergence times for nodes resolved as concordant between the reference and target. Given the computed set of secondary calibrations, post hoc tree transformation can be accomplished using existing resources that assume either a strict or relaxed evolutionary clock.
3. Our software is provided open source in the geiger package (http://cran.r-project.org/package=geiger) and is thoroughly demonstrated in the Supporting Information.

1. Volunteer-based ‘citizen science’ schemes now play a valuable role in deriving biodiversity indicators, both aiding the development of conservation policies and measuring the success of management. We provide a new method for analysing such data based on counts of invertebrate species characterised by highly variable numbers within a season combined with a substantial proportion of proposed survey visits not made.
2. Using the UK Butterfly Monitoring Scheme (UKBMS) for illustration, we propose a two-stage model that makes more efficient use of the data than previous analyses, whilst accounting for missing values. Firstly, generalised additive models were applied separately to data from each year to estimate the annual seasonal flight patterns. The estimated daily values were then normalised to estimate a seasonal pattern that is the same across sites but differs between years. A model was then fitted to the full set of annual counts, with seasonal values as an offset, to estimate annual changes in abundance accounting for the varying seasonality.
3. The method was tested and compared against the current approach and a simple linear interpolation using simulated data, parameterised with values estimated from UKBMS data for three example species. The simulation study demonstrated accurate estimation of linear time trends and improved power for detecting trends compared with the current model.
4. Comparison of indices for species covered by the UKBMS under the various model approaches showed similar predicted trends over time, but confidence intervals were generally narrower for the two-stage model.
5. In addition to creating more robust trend estimates, the new method allows all volunteer records to contribute to the indices and thus incorporates data from more populations within the geographical range of a species. On average, the current model only enables data from 60% of 10 km² grid squares with monitored sites to be included, whereas the two-stage model uses all available data and hence provides full coverage at least of the monitored area. As many invertebrate species exhibit similar patterns of emergence or voltinism, our two-stage method could be applied to other taxa.

1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models.
2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed.
3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation.
4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data.

1. Mark–recapture studies that rely on multiple marks to identify individuals pose modeling challenges if the marks for each individual are not always linked. If an individual with unlinked marks is encountered on two occasions and different marks are observed, then it will appear that two different individuals were captured. Failing to account for these missed matches will produce incorrect inference.
2. Madon et al. (Methods in Ecology and Evolution 2011; 2: 390) proposes a modification of the Jolly-Seber estimator for such data computed by adjusting the observed counts of individuals first captured, recaptured or not captured but known to be alive on each occasion. The adjustment involves multiplying each of these counts by a constant factor, , intended to correct for double counting of individuals and constrained between 0 and 1. Results of a simulation study provided in Madon et al. (Methods in Ecology and Evolution 2011; 2: 390) show that the proposed estimator is almost unbiased, but its uncertainty is underestimated and the true coverage of confidence intervals is consistently below the nominal value.
3. I compute separate adjustment factors for each of the counts and show (i) that a constant adjustment is not appropriate and (ii) that the theoretical adjustment factor is sometimes >1. I believe that the use of a single adjustment factor between 0 and 1 is what causes the uncertainty to be underestimated and that complete models of the observation process are required to obtain valid results.

1. Phylogenetic diversity (PD) depends on sampling depth, which complicates the comparison of PD between samples of different depth. One approach to dealing with differing sample depth for a given diversity statistic is to rarefy, which means to take a random subset of a given size of the original sample. Exact analytical formulae for the mean and variance of species richness under rarefaction have existed for some time, but no such solution exists for PD.
2. We have derived exact formulae for the mean and variance of PD under rarefaction. We confirm that these formulae are correct by comparing exact solution mean and variance to that calculated by repeated random (Monte Carlo) subsampling of a data set of stem counts of woody shrubs of Toohey Forest, Queensland, Australia. We also demonstrate the application of the method using two examples: identifying hot spots of mammalian diversity in Australasian ecoregions and characterizing the human vaginal microbiome.
3. There is a very high degree of correspondence between the analytical and random subsampling methods for calculating mean and variance of PD under rarefaction, although the Monte Carlo method requires a large number of random draws to converge on the exact solution for the variance.
4. Rarefaction of mammalian PD of ecoregions in Australasia to a common standard of 25 species reveals very different rank orderings of ecoregions, indicating quite different hot spots of diversity than those obtained for unrarefied PD. The application of these methods to the vaginal microbiome shows that a classical score used to quantify bacterial vaginosis is correlated with the shape of the rarefaction curve.
5. The analytical formulae for the mean and variance of PD under rarefaction are both exact and more efficient than repeated subsampling. Rarefaction of PD allows for many applications where comparisons of samples of different depth are required.

1. Stable isotope analysis is often used to identify the relative contributions of various food resources to a consumer's diet. Some Bayesian isotopic mixing models now incorporate uncertainty in the isotopic signatures of consumers, sources and trophic enrichment factors (e.g. SIAR, MixSIR). This had made model outputs more comprehensive, but at the expense of simple model evaluation, and there is no quantitative method for determining whether a proposed mixing model is likely to explain the isotopic signatures of all consumers, before the model is run.
2. Earlier linear mixing models (e.g. IsoSource) are easier to evaluate, such that if a consumer's isotopic signature is outside the mixing polygon bounding the proposed dietary sources, then mass balance cannot be established and there is no logical solution. This can be used to identify consumers for exclusion or to reject a model outright. This point-in-polygon assumption is not inherent in the Bayesian mixing models, because the source data are distributions not average values, and these models will quantify source contributions even when the solution is very unlikely.
3. We use a Monte Carlo simulation of mixing polygons to apply the point-in-polygon assumption to these models. Convex hulls (‘mixing polygons’) are iterated using the distributions of the proposed dietary sources and trophic enrichment factors, and the proportion of polygons that have a solution (i.e. that satisfy point-in-polygon) is calculated. This proportion can be interpreted as the frequentist probability that the proposed mixing model can calculate source contributions to explain a consumer's isotopic signature. The mixing polygon simulation is visualised with a mixing region, which is calculated by testing a grid of values for point-in-polygon.
4. The simulation method enables users to quantitatively explore assumptions of stable isotope analysis in mixing models incorporating uncertainty, for both two- and three-isotope systems. It provides a quantitative basis for model rejection, for consumer exclusion (those outside the 95% mixing region) and for the correction of trophic enrichment factors. The simulation is demonstrated using a two-isotope study (¹⁵N, ¹³C) of an Australian freshwater food web.

1. Info-gap decision theory (IGDT) seeks to provide a framework for rational decision-making in situations of severe uncertainty. The theory proposes non-probabilistic models of uncertainty and requires relatively small information inputs when compared to alternative theories of uncertainty.
2. Info-gap decision theory has been criticised because it is based upon models that do not guarantee good decisions in situations of severe uncertainty, where severe means a ‘very large’ uncertainty space and very poor initial estimates of the unknown elements in this space.
3. This paper reviews the use of this method in ecology where it is receiving interest in applied environmental management applications. Paradoxically, ecological applications of IGDT focus almost exclusively on only one source of uncertainty in ecological problems, model parameter uncertainty, and typically ignore other sources, particularly model structure uncertainty and dependence between parameters, that can be just as severe.
4. Ecologists and managers contemplating the use of IGDT should carefully consider its strengths and weaknesses, reviewed here, and not turn to it as a default approach in situations of severe uncertainty, irrespective of how this term is defined. We identify four areas of concern for IGDT in practice: sensitivity to initial estimates, localised nature of the analysis, arbitrary error model parameterisation and the ad hoc introduction of notions of plausibility.

1. In age-structured populations, environmental autocorrelations influence the long-run population growth rate as well as the variance in future population size. We used the concept of individual reproductive value to examine how autocorrelated environments affect the dynamics of age-structured populations, leading to transparent interpretations and estimation of these effects.
2. Environmental autocorrelation is expressed by the covariances between mean individual reproductive values for each age class and size of the same age class with stochastic components depending only on noise matrices from previous years. Thus, if an age class that is large in a given year also tends to perform better than the temporal average of that class in the contribution per individual to future population sizes, then the environmental autocorrelation will be positive.
3. We use a simple model with temporal autocorrelation in recruitment rate to illustrate the theory through analytical results as well as stochastic simulations. We show how the effect of environmental autocorrelation, the term included in the long-run growth rate, as well as influencing the variance of future population size, can be estimated using a combination of individual-based demographic data and time series of fluctuations in age composition without estimating autocorrelations and cross-correlations of large numbers of age-specific vital rates.
4. The method was applied to data from four mammal species. These analyses revealed that the influence of autocorrelations in the environmental noise on the dynamics of these species was small and in two populations almost negligible.
5. The theoretical explorations as well as the empirical estimates indicate that the temporal scaling of the environmental autocorrelation must be long to substantially affect the long-term population growth rate. The white noise approximation is therefore often very accurate.

1. Approximate Bayesian computation (ABC), a type of likelihood-free inference, is a family of statistical techniques to perform parameter estimation and model selection. It is increasingly used in ecology and evolution, where the models used can be too complex to be handled with standard likelihood techniques. The essence of ABC techniques is to compare simulation outputs to observed data, in order to select the parameter values of the simulations which best fit the data. ABC techniques are thus computationally demanding. This constitutes a key limitation to their implementation.
2. We introduce the R package ‘EasyABC’ that enables one to launch a series of simulations from the R platform and to retrieve the simulation outputs in an appropriate format for post-processing. The ‘EasyABC’ package further implements several efficient parameter sampling schemes to speed up the ABC procedure: on top of the standard prior sampling, it implements various algorithms to perform sequential (ABC-sequential) and Markov chain Monte Carlo (ABC-MCMC) sampling schemes. The package functions can furthermore make use of parallel computing.
3. The R package ‘EasyABC’ complements the package ‘abc’ which enables various post-processing of simulation outputs. ‘EasyABC’ makes several state-of-the-art ABC implementations available to the large community of R users in the fields of ecology and evolution. It is a freely available R package under the GPL license, and it can be downloaded at http://cran.r-project.org/web/packages/EasyABC/index.html.

1. The standard spatial capture–recapture design for sampling animal populations uses a fixed array of detectors, each operated for the same time. However, methods are needed to deal with the unbalanced data that may result from unevenness of effort due to logistical constraints, partial equipment failure or pooling of data for analysis.
2. We describe adjustments for varying effort for three types of data each with a different probability distribution for the number of observations per individual per detector per sampling occasion. A linear adjustment to the expected count is appropriate for Poisson-distributed counts (e.g. faeces per searched quadrat). A linear adjustment on the hazard scale is appropriate for binary (Bernoulli-distributed) observations at either traps or binary proximity detectors (e.g. automatic cameras). Data pooled from varying numbers of binary detectors have a binomial distribution; adjustment is achieved by varying the size parameter of the binomial.
3. We compared a hazard-based adjustment to a more conventional covariate approach in simulations of one temporal and one spatial scenario for varying effort. The hazard-based approach was the more parsimonious and appeared more resistant to bias and confounding.
4. We analysed a dataset comprising DNA identifications of female grizzly bears Ursus arctos sampled asynchronously with hair snares in British Columbia in 2007. Adjustment for variation in sampling interval had negligible effect on density estimates, but unmasked an apparent decline in detection probability over the season. Duration-dependent decay in sample quality is an alternative explanation for the decline that could be included in future models.
5. Allowing for known variation in effort ensures that estimates of detection probability relate to a consistent unit of effort and improves the fit of detection models. Failure to account for varying effort may result in confounding between effort and density variation in time or space. Adjustment for effort allows rigorous analysis of unbalanced data with little extra cost in terms of precision or processing time. We suggest it should become routine in capture–recapture analyses. The methods have been made available in the R package secr.

1. Annular chambers represent a novel approach for thermal preference experiments in aquatic ectothermic organisms. Most approaches using annular chambers so far lack automation in data recording and analysis, making temperature preference experiments laborious and time consuming.
2. Here, we describe the design and construction of a modified version of an annular chamber system. We conducted extensive tests to improve the systems' functionality and confirm accuracy of the thermal gradient. Additionally, we present an automated matlab routine for data recording and analysis of temperature preference experiments using the common brown shrimp (Crangon crangon, L.) as a test organism. Using this automated routine, we performed an in silico comparison of different thermal gradient representations with various complexities to test for the effect of temperature resolution on the accuracy of thermal preference estimates.
3. The here presented annular chamber produced a stable thermal gradient of ∆23 °C, ranging between 3 and 25 °C. Automated recording and data analysis facilitated implementation of long-term experiments and allowed the collection of highly resolved preference data. The in silico comparison revealed a more accurate specification of the preference zone with increasing resolution of the temperature gradient. With regard to spatial resolution of the thermal gradient and assignment of position and temperature data, the in silico comparison demonstrated previous approaches to be inappropriate for benthic and passive species.
4. We present guidelines for annular chamber construction and automation of data analysis in these systems, making annular chambers more handy and applicable for a wide range of preference studies. Besides its use for experiments in annular chambers, the principle of the here presented automated matlab routine can be applied to a wide range of behavioural and preference studies.

1. Clustering multivariate species data can be an effective way of showing groups of species or samples with similar characteristics. Most current techniques classify the samples first and then the species. A disadvantage of classifying the samples first is that relatively subtle differences between occurrence profiles of species can be obscured.
2. The k-means method of clustering minimizes the sum of squared distances between cluster centres and cluster members. If the entities to be clustered are projected on the unit sphere, then a natural measure of dispersion is the sum of squared chord distances separating the entities from their cluster centres; k-means clustering with this measure of dispersion is called spherical k-means (SKM). We also consider a variant in which the sum of squared perpendicular distances to a central ray is minimized.
3. Unweighted SKM is liable to produce clusters of very rare species. This feature can be avoided if each point on the unit sphere is weighted by the length of the ray that produced it. The standard SKM algorithm converges to very numerous local optima. To avoid this problem, we have developed a computationally intensive algorithm that uses multiple randomizations to select high-quality seed species.
4. The species clustering can be used to define simplified attributes for the samples. If the samples are then classified using the same technique, the resulting matrix of clustered species and clustered samples provides a biclustering of the data. The strength of the relationship between clusters can be measured by their mutual information, which is effectively the entropy of the biclustering.
5. The technique was tested on five ecological and biogeographical datasets ranging in size from 30 species in 20 samples to 1405 species in 3857 samples. Several variants of SKM were compared, together with results from the established program Twinspan. When judged by entropy, SKM always performed adequately and produced the best clustering in all datasets but the smallest.

1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection.
2. In this paper we develop the first statistical framework for jointly modeling space usage, resource selection, and population density by integrating SCR data, such as from camera traps, mist-nets, or conventional catch traps, with resource selection data from telemetered individuals. We provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the SCR and RSF models.
3. Our method leads to increases in precision for estimating parameters of ordinary SCR models. Importantly, we also find that SCR models alone can estimate parameters of RSFs and, as such, SCR methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available.
4. Finally, we find that SCR models using standard symmetric and stationary encounter probability models may not fully explain variation in encounter probability due to space usage, and therefore produce biased estimates of density when animal space usage is related to resource selection. Consequently, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture–recapture methods.

1. Simulation models are essential tools in landscape genetics to study how genetic processes are affected by landscape heterogeneity. However, there is still a need to develop different simulation approaches in landscape genetics, so that users may dispose of additional programs to explore further the impact of land-use and land-cover changes on population genetics.
2. We developed a spatially explicit, individual-based, forward-time, landscape-genetic simulation model combined with a landscape cellular automaton to represent evolutionary processes of adaptation and population dynamics in changing landscapes, using the NetLogo environment.
3. This simulation model represents a unique tool for scientists and scholars looking for a practical and pedagogical framework to explore both empirical and theoretical situations.

1. Concerns regarding the rapid loss of endemic biodiversity, and introduction and spread of non-indigenous species, have focused attention on the need and ability to detect species present in communities at low abundance. However, detection of rare species poses immense technical challenges, especially for morphologically cryptic species, microscopic taxa and those beneath the water surface in aquatic ecosystems.
2. Next-generation sequencing technology provides a robust tool to assess biodiversity, especially for detection of rare species. Here, we assess the sensitivity of 454 pyrosequencing for detection of rare species using known indicator species spiked into existing complex plankton samples. In addition, we develop universal small subunit ribosomal DNA primers for amplification of a wide range of taxa for detailed description of biodiversity in complex communities.
3. A universality test of newly designed primers for the hypervariable V4 region of the nuclear small subunit ribosomal DNA (V4-nSSU) using a plankton sample collected from Hamilton Harbor showed that 454 pyrosequencing based on this universal primer pair can recover a wide range of taxa, including animals, plants (algae), fungi, blue-green algae and protists.
4. A sensitivity test showed that 454 pyrosequencing based on newly designed universal V4-nSSU primers was extremely sensitive for detection of very rare species. Pyrosequencing was able to recover spiked indicator species with biomass percentage as low as approximately 2·3 × 10⁻⁵% when 24 artificially assembled samples were tagged and sequenced in one PicoTiter plate (i.e. sequencing depth of an equivalent of 1/24 PicoTiter plate). In addition, spiked rare species were sometimes recovered as singletons (i.e. Operational Taxonomic Units represented by a single sequence), suggesting that at least some singletons are informative for recovering unique lineages in ‘rare biospheres’.
5. The method established here allows biologists to better investigate the composition of aquatic communities, especially for detection of rare taxa. Despite a small-scale pyrosequencing effort, we demonstrate the extreme sensitivity of pyrosequencing using rare species spiked into plankton samples. We propose that the method is a powerful tool for detection of rare native and/or alien species.

1. Dissimilarity measures can be formulated using matching components that can be defined as the intersection in terms of species composition of both sets (a) and the relative complements of each set (b and c respectively). Previous work has extended these matching components to abundance-based measures of dissimilarity.
2. Using these matching components in terms of species abundances I provide a novel partition separating two components of abundance-based dissimilarity: (i) balanced variation in abundance, whereby the individuals of some species in one site are substituted by the same number of individuals of different species in another site; and (ii) abundance gradients, whereby some individuals are lost from one site to the other.
3. New indices deriving from the additive partition of Bray-Curtis dissimilarity are presented, each one accounting separately for these two antithetic components of assemblage variation.
4. An example comparing the patterns of increase of assemblage dissimilarity with spatial distance in two tropical forests is provided to illustrate the usefulness of the novel partition to discern the different sources of assemblage variation.
5. The widely used Bray-Curtis index of dissimilarity is the result of summing these two sources of dissimilarity, and therefore might consider equivalent patterns that are markedly different. Therefore, the novel partition may be useful to assess biodiversity patterns and to explore their causes, as substitution and loss of individuals are patterns that can derive from completely different processes.

1. Seed dispersal, a critical phase in the life history of many plants, is poorly understood due to the difficulty of tracking and monitoring dispersing seeds until they reach their ultimate fate. Scatter-hoarding rodents play a substantial part in the seed dispersal process of many plant species, however, existing tracking methods do not allow seed monitoring without risk of influencing the hoarding process and seed fate.
2. Here, we describe and test the use of Passive Integrated Transponders (PIT) tags inserted into seeds for the tracking and monitoring of large seeds dispersed by rodents. Unlike other tagging methods, PIT tagging combines the advantages of leaving no external cues and being readable without disturbance of caches. Rodents cannot remove these tags.
3. We evaluated the performance of PIT tagging through a series of trials with Quercus acorns dispersed by rodents, both in North America and in Europe, with equipment from different manufacturers. We quantified effects of tagging on seed removal and caching, cache pilferage and seed germination, by comparison between PIT-tagged and untagged acorns. We evaluated the detectability of buried tags to researchers.
4. Minimal effects of PIT tagging on seed removal, caching, pilferage and germination were found. Buried PIT tags were retrieved with high reliability by naïve researchers, even at burial depths up to 30 cm. Identification codes could be read even when multiple tags were buried at a single location, as in larder hoarding.
5. The method was successfully applied in two field studies of dispersal of Quercus palustris and Q. rubra acorns by Eastern grey squirrels Sciurus carolinensis in North America, and Q. robur acorns by Wood mice Apodemus sylvaticus in the Netherlands. The proportion of seeds recovered was comparable to that in studies using traditional thread tags.
6. We conclude that PIT tagging is a particularly suitable method for tracking and monitoring of seeds dispersed by scatter-hoarding rodents. PIT tagging solves most of the main problems generally encountered when following the fate of rodent-dispersed seeds over time.

1. It is widely recognized that macroecological patterns are not independent of the evolution of the lineages involved in generating these patterns. While many researchers have begun to evaluate the effect of ancestor–descendant relationships on observed patterns using the phylogenetic comparative method, most macroecological studies only utilize the cross-sectional comparative method to ‘remove the phylogenetic history’, without considering the option of evaluating its effect without removing it.
2. Currently, most researchers use this method without explicitly evaluating three fundamental evolutionary assumptions of the comparative method: (i) that the phylogeny is constructed without error (which implies evaluating phylogenetic uncertainty); (ii) that more closely related species tend to show more similar characters than expected by chance (which implies evaluating the phylogenetic signal) and; (iii) that the model of the characters' evolution effectively recapitulates their history (which implies comparing the fit of several evolutionary models and evaluating the uncertainty of the estimating model parameters).
3. Macroecological studies will benefit from the use of the comparative method to assess the effect of phylogenetic history without removing its effect. The comparative method will also allow for the simultaneous analysis of trait evolution and its impact on diversification rates; it is important to evaluate these processes together because they are not independent. In addition, explicit evaluations of the assumptions of comparative methods using Bayesian inferences will allow researchers to quantify the uncertainty of specific evolutionary hypotheses accounting for observed macroecological patterns.
4. We illustrate the usefulness of the method using the phylogeny of the genus Sebastes (Pisces: Scorpaeniformes), together with data on the body size–latitudinal range relationship to estimate the effect of phylogenetic history on the observed macroecological pattern.

1. We present a method, ‘SURFACE’, that uses the Ornstein-Uhlenbeck stabilizing selection model to identify cases of convergent evolution using only continuous phenotypic characters and a phylogenetic tree.
2. SURFACE uses stepwise Akaike Information Criterion first to locate regime shifts on a tree, then to identify whether shifts are towards convergent regimes. Simulations can be used to test the hypothesis that a clade contains more convergence than expected by chance.
3. We demonstrate the method with an application to Hawaiian Tetragnatha spiders, and present numerical simulations showing that the method has desirable statistical properties given data for multiple traits.
4. The r package surface is available as open source software from the Comprehensive R Archive Network.

1. Habitat restoration is increasing in importance as a conservation action, compared with more traditional establishment of conservation areas. It is applied, for example, in the context of biodiversity offsetting, in which environmental impacts of economic activity are offset by additional compensating conservation efforts.
2. We present a publicly available decision support software tool for the comparison of ecological impacts of alternative land-use options.
3. Methods implemented account for uncertain consequences of alternative land-use options, including conservation actions. These actions have different costs and effects on different biodiversity features, including species, guilds or habitat types, in different environments. Consequences of actions are uncertain through time, and time discounting is allowed in the investigation of temporal preferences.
4. This tool facilitates analyses relevant for planning of habitat restoration or management, environmental impact avoidance, biodiversity offsetting and scenario development for systematic conservation planning. RobOff derives its name from Robust Offsetting.

1. Bayesian state-space movement models have been proposed as a method of inferring behavioural states from movement paths (Morales et al. 2004), thereby providing insight into the behavioural processes from which patterns of animal space use arise in heterogeneous environments. It is not clear, however, how effective state-space models are at estimating behavioural states.
2. We use stochastic simulations of two movement models to quantify how behavioural state movement characteristics affect classification error. State-space movement models can be a highly effective approach to estimating behavioural states from movement paths.
3. Classification accuracy was contingent upon the degree of separation between the distributions that characterize the states (e.g. step length and turn angle distributions) and the relative frequency of the behavioural states. In the best case scenarios classification accuracy approached 100%, but was close to 0% when step length and turn angle distributions of each state were similar, or when one state was rare. Mean classification accuracy was uncorrelated with path length, but the variance in classification accuracy was inversely related to path length.
4. Importantly, we find that classification accuracy can be predicted based on the separation between distributions that characterize the movement paths, thereby providing a method of estimating classification accuracy for real movement paths. We demonstrate this approach using radiotelemetry relocation data of 34 moose (Alces alces).
5. We conclude that Bayesian state-space models offer powerful new opportunities for inferring behavioural states from relocation data.

1. Forecasting shifts in biome and species distribution is crucially needed in the current context of global change. So far, most projections of vegetation distribution rely on correlative species distribution models (SDMs). Yet, process-based or hybrid models based on explicit physiological description may be more robust to extrapolation under future climatic conditions. Differences between model projections may be wide, leading to scepticism among environmental stakeholders.
2. Here, we propose to combine outputs of several distribution models based on physiological responses, to produce both consensual maps of occurrences and maps of associated uncertainty. The consensus map relies on the conditional projections of each SDM. Because the models used are based on processes, their errors are likely to vary consistently with climate as some processes not implemented in a model might be important under a given set of climatic conditions. Uncertainty of the consensus model is thus assessed through multimodel regression of deviance maps with respect to current climatic conditions, and can be extrapolated to forecast climates.
3. We illustrate this approach using three SDMs, on three widely distributed European trees (Fagus sylvatica L., Quercus robur L. and Pinus sylvestris L.), and project their distributions under two scenarios. The conditional consensus outperforms classical methods of model consensus (i.e. to use the mean, the median or a weighted average of individual SDM outputs) in projecting current occurrences.
4. Consistently, with the results of individual SDMs, the conditional consensus projects that the suitable areas for F. sylvatica and Q. robur will expand towards north-eastern Europe, while that of P. sylvestris will contract. Projections of future occurrence are most uncertain towards the margins of the distribution (particularly the trailing edge).
5. Our approach can help modellers identify the limitations of each SDM and stakeholders pinpoint the regions of models agreement and highest certainty.

1. The improvement of tools for protecting biodiversity requires integrating habitat connectivity to build efficient ecological networks that facilitate the movement of species under pressure from global change. Several methodological and scientific challenges are faced in constructing such networks. First, ecological networks need to incorporate habitat connectivity for species with different ecological requirements. Secondly, the networks should be based on functional connectivity rather than on structural connectivity alone. Thirdly, connectivity needs to be treated as a continuous variable.
2. We propose a non-oriented approach of landscape description to identify favourable areas and measure functional connectivity for multi-specific applications, using three groups of common bird species (farmland specialists, forest specialists and generalists) as indicators of biodiversity.
3. In the highly anthropized region of Seine-et-Marne, we defined 20 landscape types based on composition and configuration. We used statistical modelling to obtain a value of favourability for each landscape type for each bird group. We then mapped landscape favourability, for the three groups in 1982 and 2003 to identify favourable entities (adjacent favourable landscape units) and determine connectivity. We then examined temporal changes in the favourable areas and their connectivity and determined the sensitivity of the favourable landscape types to land cover change.
4. Composition and configuration both influenced landscape favourability. Some landscape types were favourable for several groups of species and could potentially serve as junction landscapes in ecological networks that accommodate a variety of ecological requirements. Increasing urbanization and fragmentation between 1982 and 2003 resulted in a decrease in favourable landscape units, as well as consequent decreases in favourable areas and connectivity, for the three species groups. Connectivity loss was greatest for farmland and generalist species, as it was already high for forest species in 1982.
5. Such a non-oriented landscape description could be used to delineate multi-specific ecological networks at regional and national scales and could be further developed to study the connectivity of communities. The maps of favourability produced here could also be used in combination with other methods, such as graphs or circuits, to detect ecological corridors and stepping stones to habitat connectivity.

1. Metabolomics is allowing great advances in biological sciences. Recently, an increasing number of ecological studies are using a metabolomic approach to answer ecological questions (ecometabolomics). Ecometabolomics is becoming a powerful tool which allows following the responses of the metabolome of an organism environmental changes and the comparison of populations. Some Nuclear Magnetic Resonance (NMR) protocols have been published for metabolomics analyses oriented to other disciplines such as biomedicine, but there is a lack of a description of a detailed protocol applied to ecological studies.
2. Here we propose a NMR-based protocol for ecometabolomic studies that provides an unbiased overview of the metabolome of an organism, including polar and nonpolar metabolites. This protocol is aimed to facilitate the analysis of many samples, as typically required in ecological studies. In addition to NMR fingerprinting, it identifies metabolites for generating metabolic profiles applying strategies of elucidation of small molecules typically used in natural-product research, and allowing the identification of secondary and unknown metabolites. We also provide a detailed description to obtain the numerical data from the ¹H-NMR spectra needed to perform the statistical analyses.
3. We tested and optimized this protocol by using two field plant species (Erica multiflora and Quercus ilex) sampled once per season. Both species showed high levels of polar compounds such as sugars and amino acids during the spring, the growing season. E. multiflora was also experimentally submitted to drought and the NMR analyses were sensitive enough to detect some compounds related to the avoidance of water loses.
4. This protocol has been designed for ecometabolomic studies. It identifies changes in the compositions of metabolites between individuals and detects and identifies biological markers associated with environmental changes.

1. Two-level data, in which level-1 units or individuals are nested within level-2 units or clusters, are very common in natural populations. However, very few multilevel analyses are conducted for data with imperfect detection of individuals. Multilevel analyses are important to quantify the variability at each level of the data.
2. In this study, we present two-level analyses for estimating demographic parameters from data with imperfect detection of individuals and with a source of individual variability that is nested within a source of cluster variability.
3. This method allows separating and quantifying the phenotypic plasticity or facultative behavioural responses from the evolutionary responses. We illustrate our approach using data from studies of a long-lived perennially monogamous seabird, the Cory's shearwater (Calonectris diomedea) and a patchy population of collared flycatchers (Ficedula albicollis).
4. We demonstrate the existence of dependence in recapture probability between paired individuals in the Cory's shearwater. In addition, we show that family structure has no influence on parent–offspring resemblance in collared flycatchers dispersal.
5. The new method is implemented in program e-surge which is freely available from the internet.

1. Experimental laboratory systems (ELS) are widely applied research tools to test theoretical predictions in ecology and evolution. Combining ELS with automated image analysis could significantly boost information acquisition due to the ease at which abundance and morphological data is collected. Despite the advantages of image analysis, the technology has not been fully adopted yet, presumably due to the difficulties of technical implementation.
2. The tools needed to integrate image analysis in ELS are nowadays readily available: digital camera equipment is purchased at limited costs and free software solutions which allow sophisticated image processing and analysis exist. Here, we give a concise description how to integrate these pieces into a largely automated image analysis workflow. We provide researchers with necessary background information on the principles of image analysis, explaining how to standardize image acquisition and how to validate the results to reduce bias.
3. Three cross-platform and open-source software solutions for image analysis are compared: ImageJ, the EBImage package in R, and Python with the SciPy/scikit image libraries. The relative strengths and limitations of each solution are compared and discussed. In addition, a set of test images and three scripts are provided in the Online Supplementary Material to illustrate the use of image analysis and help biologists to implement image analysis in their own systems.
4. To demonstrate the reliability and versatility of a validated image analysis workflow, we introduce our own Tetrahymena thermophila ELS. Then, examples from evolutionary ecology are provided showing the advantages of image analysis to study different ecological questions, aiming at both the population and individual level.
5. Experimental laboratory systems that integrate the advantages of image analysis extend their application and versatility compared with regular ELS. Such improvements are necessary to understand complex processes such as eco-evolutionary feedbacks, community dynamics and individual behaviour in ELS.

1. The measurement of steroid hormone metabolites from faeces in wild animal populations is a powerful, noninvasive tool in behavioural endocrinology of all major vertebrate taxa. However, because such research is often done in remote areas with limited infrastructure, storage of samples for hormone analysis over long periods at high temperature is a critical issue in field endocrinology. Previous studies have indicated that storage of alcoholic faecal extracts is more reliable than storage of unprocessed faeces if no freezer is available, but a standard method has not been established yet.
2. We tested the validity of three commonly applied storage conditions – liquid extracts, dried extracts and extracts placed on solid-phase extraction (SPE) cartridges – to preserve concentrations of glucocorticoid and androgen metabolites from faecal extracts of olive baboons (Papio anubis) at high temperature over 1 year.
3. Temporal variation in concentrations was detected for all metabolites and all storage conditions, including values measured from the control condition, that is, extracts stored at −20°C. This suggested that most variation was due to interassay variability, corroborated by comparisons of variation in ‘quality controls’ and samples.
4. Compared to frozen control samples, liquid extracts were stable for up to 24 weeks, extracts on SPE cartridges were stable for up to 50 weeks, while steroid metabolite concentrations in dried extracts decreased slightly over time.
5. If steroid samples have to be stored at ambient temperature, we suggest storage of liquid extracts for up to 24 weeks in a dark and cool place. For longer periods, SPE cartridges should be applied as evaporation, a potential confound arising with long-term storage of liquid extracts at higher temperatures, is not a problem in this storage condition. Storage of dried extracts is more cost-effective, but may result in small time-dependent changes in steroid concentrations.