1. Phylogenetic comparative methods require species names in a trait dataset to match tip labels in a phylogenetic tree. Yet this apparently simple prerequisite is often one of the most fragile steps in a comparative workflow. Names may differ because of, for example, formatting, taxonomic revisions, synonyms, or spelling errors. If these differences are resolved informally, species can be lost from analyses, and the reasons for their loss can be difficult to reconstruct. 2. Here, we present prepR4pcm, an R package for preparing data and trees for phylogenetic comparative methods. The package reconciles species names through a staged procedure: exact matching, normalised matching, synonym lookup with local taxonomic databases, and optional fuzzy matching for likely spelling errors. Each decision is stored in a reconciliation object with the original name, matched name, match type, confidence score, and a short explanation. This object turns name matching from a hidden preprocessing step into an auditable part of the analysis. 3. prepR4pcm also supports the points where comparative workflows need human judgement. Users can inspect unresolved names, accept or reject suggested matches, add manual corrections, apply taxonomy crosswalks (which link names across taxonomic systems), compare reconciliation runs, and generate reports. The package then returns a matched data frame and pruned tree with the same species set, ready for phylogenetic generalised least squares, phylogenetic mixed models, phylogenetic meta-analysis, and related workflows. If users do not yet have a tree, prepR4pcm can retrieve trees from several sources, date trees when suitable information is available, and format tree-source citations. 4. We illustrate the workflow using bundled datasets with realistic name mismatches. prepR4pcm is available at https://github.com/itchyshin/prepR4pcm with documentation and vignettes covering data and tree reconciliation, tree retrieval, multi-tree workflows, and phylogenetic meta-analysis.

Organochlorine pesticides remain linked to adverse health outcomes, despite the widespread bans and regulations implemented during the 20th century. To better understand these links at a broader scale, researchers have leveraged meta-analyses to quantify pooled mean estimates and assess consistency across multiple primary studies. However, the rapid uptake of meta-analysis has created a diverse and largely fragmented secondary evidence base across many organochlorine pesticides and health outcomes. To consolidate and clarify this evidence base, we conducted a second-order synthesis of 40 meta-analyses, encompassing 129 meta-analytic model estimates. We examined the overall mean effect size and heterogeneity across the included meta-analyses. To ensure comparability, all effect sizes were converted to a common metric (odds ratio), and we used I2 as a common measure of heterogeneity. Our synthesis revealed that, across all included pesticides and adverse health outcomes, organochlorine pesticides increase the odds of an adverse health outcome by an average of 28% in organochlorine pesticide exposed groups compared to unexposed groups (OR = 1.279, 95% confidence interval, hereon CI = [1.16,1.41], 95% prediction interval, hereon PI = [0.660, 2.48]). Specifically, we found that DDE (OR = 1.41, CI = [1.09, 1.83], PI = [1.06, 1.08], number of meta-analyses, hereon (n): 12, number of meta-analytic model estimates, hereon (k): 17), and HCH (OR = 1.43. CI = [1.19, 1.7], PI = [1.07, 1.9]), n = 3, k = 3) exhibited the strongest associations with adverse health outcomes. Endocrine-related diseases showed the highest association with organochlorine pesticide exposure, with an average 52% increase in odds (OR = 1.52, CI = [1.18, 1.95], PI = [1,17, 1.97], n = 13, k = 31). We then revealed that on average the observed heterogeneity in each meta-analysis was moderately high across all outcomes and pesticides (I2within.MA.estimate.average = 54.8%, CI = [37.3, 67.4]). The organochlorine pesticide which exhibited the most consistent impacts was DDT (I2within.MA.estimate.average = 47.7%, CI = [22.0, 65.0]) and the most consistently impacted adverse health outcome were malignant neoplasms (I2within.MA.estimate.average = 19.2%, CI = [0.0, 50.4]. Together, our second-order synthesis quantifies the overall association between exposure to organochlorine pesticides and adverse health outcomes, and the consistency of that association across multiple pesticides and outcomes, providing valuable insights for decision makers and researchers.

Ecological syntheses (meta-analysis) usually ask "what is the average effect?", but many ecological questions also depend on whether outcomes become more or less variable and whether effects are predictable across contexts. We show how the same dataset can support a coherent workflow that separates: (i) within-population variability (dispersion among individuals or sampling units inside studies) from (ii) between-population heterogeneity (dispersion among effect sizes across studies), and targets both for mean effects and variability effects. Using the organic versus conventional crop-yield dataset as an illustration, along with an online tutorial, we analyse mean effects with the log response ratio (lnRR; Model 1) and within-population variability with the log variance ratio (lnVR) and the log coefficient of variation ratio (lnCVR; Model 2), noting that these three effect sizes can be computed from the same summary statistics (means, SDs and sample sizes). We then extend standard meta-regression to location-scale (mean-variance) modelling, allowing moderators to explain not only how lnRR (Model 3) and lnVR/lnCVR (Model 4) shift on average ("location") but also how their within-study/residual heterogeneity changes with context ("scale"), thereby distinguishing settings where effects are generalisable and transferable from those where they are strongly context-dependent. The core message is that many ecological datasets already contain sufficient information to synthesise performance (lnRR), reliability/stability (lnVR/lnCVR), and predictability (context-dependent heterogeneity; i.e., four models or meta-analyses) side by side. Doing so improves not only statistical inference but also our understanding of the changing world, making meta-analytic outputs and insights more directly decision-relevant.

It’s increasingly hard to find peer reviewers. To address the problem, we propose a universal peer-review system based on the exchange of credits.

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

Artificial intelligence (AI) is increasingly used in ecology to automate data-intensive tasks, from species identification and environmental monitoring to ecological prediction. As primary studies have proliferated, evidence syntheses reviewing AI applications have also increased, but their thematic coverage, methodological emphasis, and reporting transparency remain unclear. We conducted a systematic map, critical appraisal, and bibliometric analysis of 72 evidence syntheses published between 2017 and 2025 on AI applications across the broadly defined field of ecology. Synthesis coverage was strongly concentrated on supervised machine learning and deep learning, particularly image-based classification and prediction workflows. In contrast, reviews of AI applications using acoustic, video, sensor time-series, and multimodal data were comparatively scarce. Explicit comparisons between AI methods and conventional statistical or ecological approaches were rare, as was the synthesis of performance moderators such as data availability, class imbalance, transferability, interpretability, and computational cost. Reporting transparency was generally low to moderate, with recurrent shortcomings in protocol availability, screening and extraction reporting, search validation, language coverage, and sharing of data or code. Bibliometric analyses further indicated uneven geographic representation among authors and collaboration networks. Overall, the review literature on AI in ecology is expanding rapidly, but remains better at cataloguing applications than at evaluating when, why, and under what conditions AI methods improve ecological inference or practice. More transparent, reproducible, geographically inclusive, and benchmark-oriented reviews are needed to support robust and decision-relevant ecological informatics.

Genetic variance forms the basis for evolutionary inferences as it describes the evolutionary potential of traits. The major limitation of quantitative genetic studies is achieving sufficient power and sample sizes to estimate heritabilities with sufficient precision. This issue is especially important in the case of traits that are inherently susceptible to stochastic, nonbiological variation. Behavioural traits have long been associated with this group, often yielding quantitative estimates of genetic parameters that are subject to broad estimation errors, thereby hampering the discovery of genetic variation underlying such characters. Here, we used a well-established panel of inbred genetic lines of the fruit fly, Drosophila melanogaster, to estimate relevant genetic parameters in a range of behavioural traits associated with mobility and exploration. Using a high-throughput phenotyping approach and automated scoring of large numbers of individual animals, we provide precise estimates of the quantitative genetic background behind some basic characters associated with animal behaviour. Fruit flies turn out to harbour significant genetic variance in traits directly associated with mobility and substantially lower heritabilities of traits describing the temporal variability of Y-maze movements. Mobility traits also appeared to be only moderately genetically correlated, except for movement distance vs. variability traits, where we estimated strongly negative genetic correlations. In general, our results demonstrate the existence of evolutionary potential in behavioural trait proxies measured by high-throughput methods, additionally hinting at the potential for sex-specific effects. They also emphasise the growing importance of high-throughput phenotyping in modern behavioural biology and ecology.

Abstract Systematic reviews and meta‐analyses are key evidence synthesis methods for informing future research, interventions and policy. As the validity of their conclusions depends on the primary studies they synthesise, assessing the internal validity of the included studies is essential. In some fields, such as medicine, this is the norm and is commonly done using Risk of Bias assessment tools. Risk of Bias (RoB) assessment is however rare in ecology and evolutionary biology (EEB) even though several RoB tools have been developed in some ecological subfields and related fields. To identify potential reasons for a limited uptake, we conducted a survey of ecologists and evolutionary biologists with evidence synthesis experience and reviewed 275 journals that publish EEB research for guidelines on performing RoB or related assessments. Only 28 of 232 (12%) survey respondents had correct interpretation of the RoB concept, while 46 (40%) of 116 that had heard of RoB have confused RoB with publication bias. Just 10 (4%) had conducted a RoB assessment, most of whom found it challenging. Out of the 209 EEB journals that explicitly solicit evidence synthesis (N = 58) or reviews (N = 151), only five (2%) directly mentioned standards for conducting evidence synthesis, which include RoB or related (e.g. critical appraisal) assessments. An additional 45 (22%) journals indirectly linked to RoB or a related assessment via referring to the guidelines for reporting evidence synthesis (e.g. PRISMA), despite such reporting guidelines not providing information on how to conduct RoB assessments. To increase its uptake in EEB we recommend making RoB assessment: (1) known and recognised as an essential component of a reliable evidence synthesis by including it in training materials, and journals' and funders' guidelines and policies; (2) easy to perform by bringing the synthesis community together to determine the need for developing new or adjusting existing RoB tools; and (3) possible by further improving reporting standards for primary studies so that RoB assessment can be done on these studies. For those unfamiliar with the RoB assessment, we provide five key RoB questions that existing tools often cover. These questions can be considered to understand the basic composition of the evidence included in evidence synthesis.

Climatic gradients are thought to shape animal body size, with larger species often occurring in colder environments. Yet trait-climate relationships may reflect current climatic exposure as well as the evolutionary and biogeographic histories that shaped species’ traits. Bergmann’s rule illustrates this challenge, and birds provide a useful test because seasonal migration decouples body size from climatic exposure across breeding and non-breeding seasons. We asked whether migration weakens the interspecific Bergmann’s rule pattern by reducing the negative temperature-body mass slope, or whether migrants instead differ mainly in average body mass. We analysed body mass and seasonal range-climate data for over 10,000 bird species with phylogenetic linear models, comparing resident and migratory species under breeding- and non-breeding-season climate assignments. Body mass declined with temperature in both residents and migrants under both assignments. Migrants showed a slightly shallower slope, but support for a migrant-resident slope difference was limited. Instead, migrants were consistently smaller than residents after accounting for seasonal climatic exposure, and body mass was lower in migrants with larger seasonal temperature shifts. Thus, migration does not appear to erase Bergmann's rule in birds - migratory species retain a negative temperature-body mass relationship while showing a distinct shift toward smaller body size.

The diversity–productivity relationship suggests that increasing plant species could increase primary productivity, with this effect being explained in part by the suppression of plant antagonists. We conducted a global synthesis of 609 studies to investigate how plant diversity affects plants and their antagonists. Here we show that increasing plant species consistently promotes plant performance and suppresses antagonist performance in agro-ecosystems, grasslands and forests, for herbaceous and woody plants, across tropical and temperate zones, and for replacement series and additive experimental design studies. Crop diversification (for example, intercropping and cover cropping) indirectly promotes crop production through the suppression of pests. This shows that diversifying planting systems can increase productivity while reducing reliance on synthetic pesticides, offering a sustainable pathway for agriculture from subsistence to large-scale agriculture. Overall, these results suggest that crop diversification has considerable potential to support sustainable agro-ecosystems that benefit productivity while reducing reliance on synthetic pesticides. A global synthesis of >600 studies finds that across agro-ecosystems, grasslands and forests in temperate and tropical zones, increasing plant diversity has a consistently positive effect on plant performance and the suppression of antagonists.

Human disturbances are modifying animal behavior in ecosystems worldwide, with the potential to reshape species interactions. For instance, human-induced shifts in diel activity may disrupt the alignment of daily activity patterns between interacting species and destabilize temporal niche partitioning. To test this hypothesis, we leverage a global meta-analysis on the effects of human disturbance on diel activity and overlap of 480 mammalian predator-prey and intraguild predator dyads from 57 studies. We demonstrate that human disturbance has no overall effect on temporal overlap. Instead, the body mass ratios between dominant species and subordinate species shape the influence of human disturbance. When subordinates are larger than dominant species, humans compress the temporal niche (i.e., higher diel overlap), but when dominant species are larger than subordinate species, humans expand the temporal niche (i.e., lower diel overlap). These results suggest that larger bodied mammals "lose" the temporal predator-prey response race under human disturbance, with large predators experiencing less overlap with their prey, and large prey facing more overlap with their predators. As the human footprint expands globally, we can expect continued alterations to the animal temporal niche, with consequences for species interactions, population persistence, community structure, and evolutionary dynamics.

Generative artificial intelligence (AI) is rapidly becoming embedded across scientific workflows, yet mechanisms for transparently documenting its use remain fragmented and weakly enforced. Focusing on ecology and evolutionary biology as a model discipline, we systematically mapped AI-related journal policies across 230 journals and assessed article-level compliance using a large sample of recent publications. To provide a reporting background, we also synthesised author contribution guidelines. Nearly half of journals provided no guidance on AI use, and where policies existed, they were largely generic, publisher-driven, and poorly translated into reporting practice. While author contribution statements were widely adopted, explicit AI disclosures appeared in fewer than 6% of papers, even in journals with formal AI policies. Text-mining of 124 guideline documents revealed highly standardised, precautionary language emphasising responsibility and prohibitions, with minimal operational guidance on acceptable uses or disclosure formats. To address this gap, we introduce AIdIT (AI disclosure for Improved Transparency), a standardised, taxonomy-based framework for reporting AI use across all stages of the research lifecycle. AIdIT integrates structured categories of AI use, human oversight statements, and machine-readable outputs to support reproducibility, accountability, and comparability. Together, our systematic evidence synthesis and proposed framework highlight an urgent need to normalise AI transparency as a core component of open research practice.

Persistent organic pollutants such as per- and polyfluoroalkyl substances (PFAS) and organochlorine pesticides are among the most extensively studied environmental contaminants, yet the evidence base remains fragmented across primary studies and meta-analyses, limiting its ability to chart new research directions. To address this fragmentation, we developed a unified database that links primary epidemiological estimates with meta-analytic summaries of associations between PFAS and organochlorine pesticide exposures and human health outcomes. The database was constructed following a systematic five-step process encompassing (1) literature search, (2) literature screening, (3) data extraction, (4) data curation, and (5) visualisation. The final database includes 627 unique primary studies (n) providing 3,627 individual primary study estimates (k) that were synthesised in 91 meta-analyses comprising 457 pooled effect size estimates. The most frequently investigated chemicals were PFOA (CAS: 335-67-1, n = 268, k = 736) and PFOS (CAS: 1763-23-1, n = 276, k = 682) among PFAS, and DDE (CAS: 72-55-9, n = 95 k = 150) and p,p’-DDE (CAS: 72-55-9, n = 90, k = 136) among organochlorine pesticides. Metabolic disorders (ICD11: 5C8Z, n = 39, k = 333) and neoplasms of breast (ICD11: 2C6Z, n = 78, k = 309) were the most assessed health outcomes. Primary studies were most often led by authors based in the United States, whereas meta-analyses were predominantly conducted by researchers in China. Based on the structure of this database, we outline practical methodological recommendations to improve future conduct and reporting practices, and we discuss potential applications of this database to support future research in environmental health.

Anthropogenic environmental change is a major driver of global bird declines, affecting species across continents, ecosystems, and life-history strategies. As such, it has drawn much attention in both primary research studies and meta-analyses. Because meta-analyses influence scientific consensus and conservation policy, it is essential to evaluate the representativeness and transparency of this evidence. However, despite the growing number of meta-analyses , these aspects have never been assessed, creating a clear need for a comprehensive global evaluation of meta-analyses on anthropogenic impacts of birds. Here, we present the first global synthesis, including 149 meta-analyses of anthropogenic influences on birds. We analyzed their thematic, taxonomic, ecological, and geographic coverage, evaluated adherence to reporting and methodological standards, and assessed research production, collaboration, and societal visibility using bibliometric and altmetric approaches. Meta-analyses addressed a wide range of anthropogenic pressures and birds’ responses, however with uneven attention to different topics. Habitat loss and fragmentation, agriculture, and urbanisation were overrepresented across studies, while light and noise pollution, invasive species, and hunting were largely neglected. Responses focused mainly on species abundance, diversity, and reproduction, with limited attention to behaviour, movement, migration, or phenology. Taxonomic coverage was biased towards Passeriformes, and geographic coverage skewed toward North America and Europe. Reporting standards were not widely followed, and almost half of the meta-analyses would not be possible to repeat or update. Almost none of the meta-analyses were preregistered or estimated risk of bias in primary studies, though most controlled for non-independence, and tested for publication bias. Bibliometric and altmetric analyses revealed high collaboration but geographic imbalance among authors. Overall, meta-analytical research on anthropogenic impacts on birds is extensive - but thematically, taxonomically, ecologically, and geographically uneven, with suboptimal transparency. Addressing these limitations is crucial to improve the reliability, comparability, and policy relevance, ultimately supporting more effective conservation strategies for birds.

Meta-analyses in ecology and evolution often consider the magnitude of differences between groups rather than their direction. Yet, a common practice is to coerce signed effects (e.g., d and response ratio) into magnitudes by taking absolute values. This transformation induces strong upward bias and non-normal (Gaussian) sampling distributions, violating the assumptions of standard meta-analytic models. Here we introduce lnM, a log-ratio effect size for the magnitude of difference between two groups, defined from standard one-way ANOVA components. Unlike absolute-value approaches, the proposed lnM is asymptotically normal, and can be analysed, using standard multilevel meta-analysis and meta-regression with both categorical and continuous moderators. We combine theory, simulations, and worked examples to compare lnM with absolute-value approaches. We show when the delta-method and parametric single-fit bootstrap estimators for lnM perform well, and how one may assess publication bias. The lnM effect size provides a direction-free, meta-regression-friendly measure of magnitude that is applicable to both ratio- and interval-scale traits, offering a practical solution for synthesising the magnitude of ecological and evolutionary effects and beyond.

Biological differences between males and females are pervasive. Researchers often focus on sex differences in the mean or, occasionally, in variation, albeit other measures can be useful for biomedical and biological research. For instance, differences in skewness (asymmetry of a distribution), kurtosis (heaviness of a distribution's tails), and correlation (relationship between two variables) might be crucial to improve medical diagnosis and to understand natural processes. Yet, there are currently no meta-analytic ways to measure differences in these metrics between two groups. We propose three effect size statistics to fill this gap: Δsk, Δku, and ΔZr, which measure differences in skewness, kurtosis, and correlation, respectively. Besides presenting the rationale for the calculation of these effect size statistics, we conducted a simulation to explore their properties and used a large dataset of mice traits to illustrate their potential. For example, in our case study, we found that females show, on average, a greater correlation between fat mass and heart weight than males. Although calculating Δsk, Δku, and ΔZr will require large sample sizes of individual data, technological advancements in data collection create increased opportunities to use these effect size statistics. Importantly, Δsk, Δku, and ΔZr can be used to compare any two groups, allowing a new generation of meta-analyses that explore such differences and potentially leading to new insights in multiple fields of study.