Heterogeneity—the presence of meaningful variation across observations, in models, and in inferences—is a foundational concept in statistics that has many meanings. This review synthesizes the evolution of the meanings, methodologies, and interpretations of the four dominant and interconnected types of heterogeneity: (1) heteroscedasticity (non-constant variance), historically treated as a nuisance but now modeled as substantive information in fields from finance to ecology; (2) generalized heterogeneity (i.e., variation in parameters or effects), addressed via Gaussian graphical models and frailty-based network models that uncover latent subgroup structures; (3) frailty (unobserved heterogeneity), whose effects are uniquely captured in survival analysis through frailty and accelerated failure time models. and (4) covariance and dependence (i.e., structured relationships among observations), formalized theoretically by Price’s Equation and handled practically by mixed models and generalized estimating equations (GEEs). These four ways in which heterogeneity is used in contemporary statistical research illustrate a progression from controlling variation to learning from it, and can be embedded in a broader ontology (hierarchical taxonomy) of types and sub- types of heterogeneity that span observational, model-based, and inferential domains. Mixed-effects models, Bayesian methods, causal forests, and AI-enhanced survival models are unifying platforms for jointly modeling different types of heterogeneity. Examples from applied sciences that use statistics extensively illustrate how heterogeneity has been transformed from a statistical nuisance into a source of scientific discovery. Advances in estimation, diagnostics, and causal interpretation have made meta-analysis into an exemplar for quantifying and investigating between-study heterogeneity. We conclude with practical guidelines for diagnosing, modeling, and reporting heterogeneity, and identify future challenges for dealing with heterogeneity in causal attribution, high-dimensional data, interpretability, and interdisciplinary integration. Embracing heterogeneity as a fundamental feature of complex systems represents a maturation of statistical science whose application from generalizable models to personalized medicine can provide more nuanced insights into the interpretation of complex datasets.

Ongoing environmental change is forecast to lead to lower precipitation and concomitant species losses in tropical regions. These losses may affect generalist species that provide essential ecosystem services, such as controlling the rate at which nutrients become available for uptake by other organisms in tropical forests. Here, we use a long-term (16 years) rainwater exclusion experiment in a primary Amazonian tropical rainforest (Caxiaun & atilde; National Forest, Northern Brazil) to test whether induced water stress ("drought") affects the species richness of generalist ants, their abundance (i.e., nest density), and the distance at which they detect food resources (i.e., baits). The number of generalist ant species and colonies was reduced by 50% in the drought-induced plot, and ant species composition differed between the control (typical moist forest) and drought-induced plots. Although ants that nested in both control and drought plots had shorter estimated foraging distances than habitat specialists, the distance at which these colonies detected baits was not affected by drought. We conclude that the extremely high diversity of tropical forest ants may be able to buffer the detrimental effects of drought on the resource detection rates of generalist ants. Different generalist ant species were also functionally similar to wet-forest species that cannot forage under drier conditions.

Conventional species distribution models (SDMs) typically consider only abiotic factors, thus overlooking critical biotic dimensions, including traits that play an important role in determining species' distributions in changing environments. Process-based trait SDMs explicitly incorporate traits and have been applied to SDMs. However, their parameterization can be complex and require data that are unavailable for most species. Recently developed hierarchical trait-based SDMs use widely available data and facilitate the incorporation of traits into SDMs at broad temporal, spatial, and taxonomic scales. However, despite their promise, existing hierarchical trait-based SDMs fail to accommodate changing trait spaces under different climate conditions. Here, we provide a new, simplified framework for hierarchical trait-based SDMs that integrates individuals' trait responses into forecasts of species range shifts in response to ongoing climate changes. We further briefly discuss the issue of non-independence among species in hierarchical trait-based SDMs. This work will contribute to an improved understanding of how traits affect species distributions along environmental and temporal gradients and facilitate the application of trait-based SDMs at large scales under future climate change.

Climate change is altering the timing of species' life-cycle events (i.e., phenology), but the rates of phenological shifts vary across taxa. These mismatches in phenological response may disrupt interactions between interdependent species, such as plants and their pollinators, which may lead to reduced plant reproduction via pollen limitation and thus contribute to secondary extinction risks for plants. However, secondary extinction risk is rarely assessed under future climate-change scenarios. Here, we used ca. 15,000 crowdsourced specimen records of Viola species and their solitary bee pollinators, spanning 120 y across the eastern United States, and integrated climate data, phenological information, and species distribution models to quantify the risk of secondary plant extinction associated with phenological mismatch with their bee pollinators. We further examined geographical patterns in secondary extinction risk for plants and explored how their interactions between plants and generalist versus specialist pollinators influence such risk. Secondary local extinction risk of Viola spp. increases with latitude, indicating that future climate change will pose a greater threat to plant-bee pollinator networks at northern latitudes. Additionally, the sensitivity of secondary local extinction risk to phenological mismatch with both generalist and specialist bee pollinators varies by latitude, with specialist bees showing a sharper decline at higher latitudes. Our findings demonstrate that existing conservation priorities based solely on primary extinction risk directly caused by climate change may be insufficient to support self-sustaining populations of plants. Thus, incorporating secondary extinction risk resulting from ecological mismatches between plants and pollinators into future global conservation frameworks should be carefully considered.

Phenological response to global climate change can impact ecosystem functions. There are various data sources from which spatiotemporal and taxonomic phenological data may be obtained: mobilized herbaria, community science initiatives, observatory networks, and remote sensing. However, analyses conducted to date have generally relied on single sources of these data. Siloed treatment of data in analyses may be due to the lack of harmonization across different data sources that offer partially nonoverlapping information and are often complementary. Such treatment precludes a deeper understanding of phenological responses at varying macroecological scales. Here, we describe a detailed vision for the harmonization of phenological data, including the direct integration of disparate sources of phenological data using a common schema. Specifically, we highlight existing methods for data harmonization that can be applied to phenological data: data design patterns, metadata standards, and ontologies. We describe how harmonized data from multiple sources can be integrated into analyses using existing methods and discuss the use of automated extraction techniques. Data harmonization is not a new concept in ecology, but the harmonization of phenological data is overdue. We aim to highlight the need for better data harmonization, providing a roadmap for how harmonized phenological data may fill gaps while simultaneously being integrated into analyses.

Climate change can lead to “secondary extinction risks” for plants owing to the decoupling of life-cycle events of plants and their pollinators (i.e., phenological mismatch). However, forecasting secondary extinction risk under future climate change remains challenging. We developed a new framework to quantify plants’ secondary extinction risk associated with phenological mismatch with bees using ca. 15,000 crowdsourced specimen records of Viola species and their solitary bee pollinators spanning 120 years across the eastern United States. We further examined latitudinal patterns in secondary extinction risk and explored how latitudinal variation in plant-pollinator specialization influence this risk. Secondary extinction risk of Viola spp. increases with latitude, indicating that future climate change likely will pose a greater threat to plant-bee pollinator networks at northern latitudes. Additionally, the sensitivity of secondary extinction risk to phenological mismatch with both generalist and specialist bee pollinators decreases with latitude: specialist bees display a sharper decrease at higher latitudes. Our findings demonstrate that existing conservation priorities identified solely based on primary extinction risk directly caused by climate change may not be sufficient to support self-sustaining populations of plants. Incorporating secondary extinction risk resulting from ecological mismatches between plants and pollinators into future global conservation frameworks should be carefully considered.

Forest insect outbreaks cause large changes in ecosystem structure, composition, and function. Humans often respond to insect outbreaks by conducting salvage logging, which can amplify the immediate effects, but it is unclear whether logging will result in lasting differences in forest structure and dynamics when compared with forests affected only by insect outbreaks. We used 15 years of data from an experimental removal of Tsuga canadensis (L.) Carr. (Eastern hemlock), a foundation tree species within eastern North American forests, and contrasted the rate, magnitude, and persistence of response trajectories between girdling (emulating mortality from insect outbreak) and timber harvest treatments. Girdling and logging were equally likely to lead to large changes in forest structure and dynamics, but logging resulted in faster rates of change. Understory light increases and community composition changes were larger and more rapid in the logged plots. Tree seedling and understory vegetation abundance increased more in the girdled plots; this likely occurred because seedlings grew rapidly into the sapling- and tree-size classes after logging and quickly shaded out plants on the forest floor. Downed deadwood pools increased more after logging but standing deadwood pools increased dramatically after girdling. Understory light levels remained elevated for a longer time after girdling. Perhaps because the window of opportunity for understory species to establish was longer in the girdled plots, total species richness increased more in the girdled than logged plots. Despite the potential for greater diversity in the girdled plots, Betula lenta L. (black birch) was the most abundant tree species recruited into the sapling- and tree-size classes in both the girdled and logged plots and is poised to dominate the new forest canopy. The largest difference between the girdling and logging treatments-deadwood structure and quantity-will persist and continue to bolster aboveground carbon storage and structural and habitat diversity in the girdled plots. Human responses to insect outbreaks hasten forest reorganization and remove structural resources that may further alter forest response to ongoing climate stress and future disturbances.

Computing the agreement between 2 continuous sequences is of great interest in statistics when comparing 2 instruments or one instrument with a gold standard. The probability of agreement quantifies the similarity between 2 variables of interest, and it is useful for determining what constitutes a practically important difference. In this article, we introduce a generalization of the PA for the treatment of spatial variables. Our proposal makes the PA dependent on the spatial lag. We establish the conditions for which the PA decays as a function of the distance lag for isotropic stationary and nonstationary spatial processes. Estimation is addressed through a first-order approximation that guarantees the asymptotic normality of the sample version of the PA. The sensitivity of the PA with respect to the covariance parameters is studied for finite sample size. The new method is described and illustrated with real data involving autumnal changes in the green chromatic coordinate (Gcc), an index of "greenness" that captures the phenological stage of tree leaves, is associated with carbon flux from ecosystems, and is estimated from repeated images of forest canopies.

Forecasting the impacts of changing climate on the phenology of plant populations is essential for anticipating and managing potential ecological disruptions to biotic communities. Herbarium specimens enable assessments of plant phenology across broad spatiotemporal scales. However, specimens are collected opportunistically, and it is unclear whether their collection dates – used as proxies of phenological stages – are closest to the onset, peak, or termination of a phenophase, or whether sampled individuals represent early, average, or late occurrences in their populations. Despite this, no studies have assessed whether these uncertainties limit the utility of herbarium specimens for estimating the onset and termination of a phenophase. Using simulated data mimicking such uncertainties, we evaluated the accuracy with which the onset and termination of population‐level phenological displays (in this case, of flowering) can be predicted from natural‐history collections data (controlling for biases in collector behavior), and how the duration, variability, and responsiveness to climate of the flowering period of a species and temporal collection biases influence model accuracy. Estimates of population‐level onset and termination were highly accurate for a wide range of simulated species' attributes, but accuracy declined among species with longer individual‐level flowering duration and when there were temporal biases in sample collection, as is common among the earliest and latest‐flowering species. The amount of data required to model population‐level phenological displays is not impractical to obtain; model accuracy declined by less than 1 day as sample sizes rose from 300 to 1000 specimens. Our analyses of simulated data indicate that, absent pervasive biases in collection and if the climate conditions that affect phenological timing are correctly identified, specimen data can predict the onset, termination, and duration of a population's flowering period with similar accuracy to estimates of median flowering time that are commonplace in the literature.

Phenology varies widely over space and time because of its sensitivity to climate. However, whether phenological variation is primarily generated by rapid organismal responses (plasticity) or local adaptation remains unresolved. Here we used 1,038,027 herbarium specimens representing 1,605 species from the continental United States to measure flowering-time sensitivity to temperature over time (Stime) and space (Sspace). By comparing these estimates, we inferred how adaptation and plasticity historically influenced phenology along temperature gradients and how their contributions vary among species with different phenology and native climates and among ecoregions differing in species composition. Parameters Sspace and Stime were positively correlated (r = 0.87), of similar magnitude and more frequently consistent with plasticity than adaptation. Apparent plasticity and adaptation generated earlier flowering in spring, limited responsiveness in late summer and delayed flowering in autumn in response to temperature increases. Nonetheless, ecoregions differed in the relative contributions of adaptation and plasticity, from consistently greater importance of plasticity (for example, southeastern United States plains) to their nearly equal importance throughout the season (for example, Western Sierra Madre Piedmont). Our results support the hypothesis that plasticity is the primary driver of flowering-time variation along temperature gradients, with local adaptation having a widespread but comparatively limited role. Using a large sample of herbarium records containing flowering-time data for 1,605 species of plants from the United States, the authors show that most changes in phenology in response to temperature change are attributable to phenotypic plasticity rather than local adaptation.

Abstract Large language models (LLMs) are a type of artificial intelligence (AI) that can perform various natural language processing tasks. The adoption of LLMs has become increasingly prominent in scientific writing and analyses because of the availability of free applications such as ChatGPT. This increased use of LLMs not only raises concerns about academic integrity but also presents opportunities for the research community. Here we focus on the opportunities for using LLMs for coding in ecology and evolution. We discuss how LLMs can be used to generate, explain, comment, translate, debug, optimise and test code. We also highlight the importance of writing effective prompts and carefully evaluating the outputs of LLMs. In addition, we draft a possible road map for using such models inclusively and with integrity. LLMs can accelerate the coding process, especially for unfamiliar tasks, and free up time for higher level tasks and creative thinking while increasing efficiency and creative output. LLMs also enhance inclusion by accommodating individuals without coding skills, with limited access to education in coding, or for whom English is not their primary written or spoken language. However, code generated by LLMs is of variable quality and has issues related to mathematics, logic, non‐reproducibility and intellectual property; it can also include mistakes and approximations, especially in novel methods. We highlight the benefits of using LLMs to teach and learn coding, and advocate for guiding students in the appropriate use of AI tools for coding. Despite the ability to assign many coding tasks to LLMs, we also reaffirm the continued importance of teaching coding skills for interpreting LLM‐generated code and to develop critical thinking skills. As editors of MEE, we support—to a limited extent—the transparent, accountable and acknowledged use of LLMs and other AI tools in publications. If LLMs or comparable AI tools (excluding commonly used aids like spell‐checkers, Grammarly and Writefull) are used to produce the work described in a manuscript, there must be a clear statement to that effect in its Methods section, and the corresponding or senior author must take responsibility for any code (or text) generated by the AI platform.

Summary Anthropogenetic climate change has caused range shifts among many species. Species distribution models (SDMs) are used to predict how species ranges may change in the future. However, most SDMs rarely consider how climate‐sensitive traits, such as phenology, which affect individuals' demography and fitness, may influence species' ranges. Using > 120 000 herbarium specimens representing 360 plant species distributed across the eastern United States, we developed a novel ‘phenology‐informed’ SDM that integrates phenological responses to changing climates. We compared the ranges of each species forecast by the phenology‐informed SDM with those from conventional SDMs. We further validated the modeling approach using hindcasting. When examining the range changes of all species, our phenology‐informed SDMs forecast less species loss and turnover under climate change than conventional SDMs. These results suggest that dynamic phenological responses of species may help them adjust their ecological niches and persist in their habitats as the climate changes. Plant phenology can modulate species' responses to climate change, mitigating its negative effects on species persistence. Further application of our framework will contribute to a generalized understanding of how traits affect species distributions along environmental gradients and facilitate the use of trait‐based SDMs across spatial and taxonomic scales.

Diversity and heterogeneity often are conflated but are fundamentally different. An aphorism proposed by Shavit and Ellison (2021; J. Phil. 118: 525-548) for distinguishing them is that 'a zoo is diverse whereas an ecosystem is heterogeneous.' That is, a zookeeper measuring diversity simply enumerates the different types of animals; interactions are not expected to occur between animals separated by fences or other barriers. In contrast, measures of heterogeneity ought to include both interspecific interactions and relationships between species and their heterogeneous habitats. Here, we use cross-scale, dual scaling-law analyses of heterogeneity and diversity of animal gut microbiomes (AGMs) to address three objectives: 1) estimate the spatial heterogeneity and diversity of animal-gut microbiomes; 2) analyze influences of phylogeny and diets on scaling of diversity and heterogeneity; 3) explore mechanistic differences between diversity and heterogeneity in AGMs. From 4903 AGM samples collected from 318 animal species covering all six classes of vertebrates and four major classes of invertebrates, we estimated that approximate to 640 000 operational taxonomic units (OTUs or 'species') make up the pool of microbial species that could inhabit animal guts, among which approximate to 8000 are relatively common and approximate to 800 are dominant. The gut of any single animal, however, includes only 0.01-0.5% of the total species pool. We applied Ma's diversity-area relationship for scaling diversity, and extend Taylor's power law and Luna et al.'s (2020; Diversity 12: 86) interaction diversity for scaling heterogeneity. At the community scale, phylogeny significantly influenced heterogeneity, but diets did not. Phylogeny and diets had limited influence on diversity at both community and landscape scales. We demonstrated that diversity and heterogeneity measure two different properties. Further, differences in scaling of diversity and heterogeneity are a result of heterogeneity being primarily the result of evolved dispersal behavior and interspecific interactions, whereas diversity results from abundances controlled on ecological time scales.

Species loss in tropical regions is forecast to occur under environmental change scenarios of low precipitation. One of the main questions is how drought will affect invertebrates, a key group for ecosystem functioning. We use 1 year of data from a long-term rainwater exclusion experiment in primary Amazonian rainforest to test whether induced water stress and covarying changes in soil moisture, soil respiration, and tree species richness, diversity, size, and total biomass affected species richness and composition (relative abundance) of ground-dwelling ants. Data on ant abundance and environmental variables were collected at two sites (control and experimental) in the Eastern Amazon. Since 2002, drought has been induced in the experimental plot by excluding 50% of normal rainfall. Ant species richness in the experiment plot was reduced and some generalist species responded positively. Ant species richness also increased in the experimental plot with increasing diversity of the plant species of the leaf litter. The relative abundance of ants differed between plots. The experimental plot was characterized by a higher frequency of generalist and other species that appeared to be favored by the reduction in rainfall. Between-plot comparisons suggested loss and changes in ant species composition in tropical forests were affected by increasing dryness. These changes could ultimately lead to cascading effects on ecosystem processes and the services they mediate.

We review the use of science by lawmakers and courts in implementing or rejecting legal rights for nature in Ecuador, India, the United States, and other jurisdictions where some type of rights of nature have been recognized in the legal system. We then use the "right to evolve" to exemplify how interdisciplinary work can (i) help courts effectively define what this right might entail; (ii) inform how it might be applied in different circumstances; and (iii) provide a template for how scientists and legal scholars can generate the interdisciplinary scholarship necessary to understand and implement the growing body of rights-of-nature laws, and environmental law more generally. We conclude by pointing to what further research is needed to understand and effectively implement the growing body of rights-of-nature laws.

SummaryUrbanization can affect the timing of plant reproduction (i.e. flowering and fruiting) and associated ecosystem processes. However, our knowledge of how plant phenology responds to urbanization and its associated environmental changes is limited.Herbaria represent an important, but underutilized source of data for investigating this question. We harnessed phenological data from herbarium specimens representing 200 plant species collected across 120 yr from the eastern US to investigate the spatiotemporal effects of urbanization on flowering and fruiting phenology and frost risk (i.e. time between the last frost date and flowering).Effects of urbanization on plant reproductive phenology varied significantly in direction and magnitude across species ranges. Increased urbanization led to earlier flowering in colder and wetter regions and delayed fruiting in regions with wetter spring conditions. Frost risk was elevated with increased urbanization in regions with colder and wetter spring conditions.Our study demonstrates that predictions of phenological change and its associated impacts must account for both climatic and human effects, which are context dependent and do not necessarily coincide. We must move beyond phenological models that only incorporate temperature variables and consider multiple environmental factors and their interactions when estimating plant phenology, especially at larger spatial and taxonomic scales.

Species distribution models (SDMs) have been central for documenting the relationship between species’ geographic ranges and environmental conditions for more than two decades. However, the vast majority of SDMs rarely consider functional traits, such as phenology, which strongly affect species’ demography and fitness. Using >120,000 herbarium specimens representing 360 plant species across the eastern United States, we developed a novel “phenology-informed” SDM that integrates dynamic phenological responses to changing climates. Compared to standard SDMs based only on abiotic variables, our phenology-informed SDMs forecast significantly lower species habitat loss and less species turnover within communities under climate change. These results suggest that phenotypic plasticity or local adaptation in phenology may help many species adjust their ecological niches and persist in their habitats during periods of rapid environmental change. By modeling historical data that link phenology, climate, and species distributions, our findings reveal how species’ reproductive phenology mediates their geographic distributions along environmental gradients and affects regional biodiversity patterns in the face of future climate change. More importantly, our newly developed model also circumvents the need for mechanistic models that explicitly link traits to occurrences for each species, thus better facilitating the deployment of trait-based SDMs across unprecedented spatial and taxonomic scales.

Methods in Ecology & Evolution (MEE) has seen four substantive changes in the 2 years since I took on the executive editorship of the journal. First, the BES policy of term-limits for associate editors and senior editors of the journals ensures their continuing evolution, and so MEE has a new team of senior editors and an editorial board with a healthy mix of rookies and veterans. Second, since January of this year, MEE has been a “gold” open-access journal, and all content published since its launch in 2010 now is either freely available (for papers previously published behind a subscription paywall) or fully open access (with either a CC-BY, CC-BY-NC or CC-BY-NC-ND licence, as the author chooses). To ensure that the author publication charge (APC) for open access does not create a barrier to publishing in the journal, the BES and Wiley provide a limited—but so far sufficient—number of APC waivers for corresponding authors who are not in countries covered by Research4Life access agreements, are not in countries or at institutions that have open-access transformational agreements with Wiley, or do not have publication costs available through their research grants. Third, MEE has joined with other top-tier journals in requiring that the data and code necessary to reproduce the methods and results reported in a manuscript submitted for review be included with the submission and then made publicly available to accompany all published papers (Jenkins et al., 2023). Finally, since July 2023, all papers submitted to MEE and the other BES journals are reviewed “double-anonymous,” where not only are the reviewers unknown to the authors but the authors also are unknown to reviewers. This change was endorsed by the BES Publications Committee based on the clear benefits of double-blind review in reducing many of the biases inherent in the previous, single-blind review process that were revealed by the detailed experimental study by Fox et al. (2023). These changes strongly support the unchanged core mission of the journal: to promote the development of new methods in ecology and evolution, to publish the best of them, and to facilitate their dissemination and uptake by the research community. In this mission, we have been extraordinarily successful. The methods we publish are widely used by ecologists and evolutionary biologists. Our 2022 Clarivate impact factor (IF) is 6.6 and, despite its known flaws, is a reasonable indicator of citation rate and use of the methods we publish. This IF ranks us 12th among the 169 “ecology” journals included in Clarivate's Journal Citation Reports, is the highest among all BES journals, and is higher than all journals published by the Nordic Society Oikos and the Ecological Society of America (other than Frontiers in Ecology and the Environment). Like all established BES journals, we receive on the order of 1000 submissions each year. More than half of these submissions are “desk-rejected” relatively rapidly (usually within 1 week) by one of the four senior editors. Only a few of these manuscripts are fatally flawed in some way; rather, most are interesting and technically sound. But they either simply are not “Methods” papers or they would fit better as a different type of paper for the journal, in which case authors may be given the option to rework and resubmit (e.g. a manuscript submitted as a full Research Article would be better—and reviewable—if it were reworked as an Application or a Practical Tools paper). On the positive side, if you actually submit a “Methods” paper of the most appropriate type, and if it makes it across a senior editor's desk, onto an associate editor's one, and is sent out for external peer review, the odds of acceptance are closer to 50%. So, what makes for a good submission and what really does not work? The best Methods papers, regardless of the article type, are about the methodology itself, not the results of the case study. A good first indicator of a Research Article that is really a Methods paper is that the Introduction identifies a gap in existing methodologies that is independent of an organism or study system. For example, using the identification of the lack of biological realism in MaxEnt species distribution models (SDMs; e.g. Adams et al., 2015; Record et al., 2018) to motivate a new type of SDM is a better lead for a Methods paper than motivating the same model based on the need to better predict the distribution of cetaceans to better manage them as the oceans warm (Becker et al., 2018). In the same vein, new models, statistical methods, and indices should be tested with simulated datasets that explore a large range of the possible parameter spaces and identify error rates. The results of these simulations almost always should appear in the main text: tests of the new methods on a real dataset (i.e. the case study) could go in the main text but could just as easily be placed in Supporting Information (SI). Our shorter article types—Applications and Practical Tools—should be similarly framed. They should fill an empty niche, not replicate or rehash an existing one. Any new method should make researchers' and practitioners' work easier, but to encourage its uptake and future use, the paper should clearly contrast the new method with those that are already available and make the case for someone to switch. As with the longer Research Articles, case studies for Applications and Practical Tools are always useful but they should not be the central focus of the manuscript. Reviews and Perspectives set benchmarks for the field and guide methodological advances. These article types are not the place to introduce new methods or models but rather to survey their existing range and identify key areas in need of additional work. Reviews and Perspectives are particularly useful for research areas where the publication of existing methods has been dispersed among many disparate journals and to call attention to methods that are less commonly used by ecologists and evolutionary biologists. A salient difference between Reviews and Perspectives is that the former can draw on a lot of published material whereas the latter cannot. Always take the time to make your manuscript shorter. Senior editors routinely find an excellent short Application or Practical Tool lurking in an overwritten, rambling Research Article. For example, if a 7000-word Research Article presents a new method that requires no new theoretical development or if simulation is unnecessary even though analytical (closed-form) solutions are unavailable, we will return it for shortening to an Application. Similarly, if a long research article is much more focused on the results of its use to, say, tracking dolphins, but we see that the tracking device potentially is a useful tool for tracking any mammal, we will return it for shortening to a Practical Tool. We would do the same for a new R or Python package that may have been developed for dolphin demographics but could be used equally well for any other marine or aquatic mammal. In either case, the “Results” in the shorter manuscript would be reduced to a brief example (at most a single figure), with details placed in SI. Finally, there are three common submission types that we rarely review or publish. First, manuscripts that describe existing statistical methods that are unappreciated by or unknown to ecologists and evolutionary biologists fit better in the Statistical Reports section of Ecology. Second, manuscripts that use existing methods in clever or unanticipated ways to analyse an interesting set of data or test a particular hypothesis are “results-oriented” and would be better submitted to one of the other BES journals. Third, manuscripts describing “workflows”—organising existing methods into a useful sequence to make one's life easier, analysing an interesting set of data, or creating a package for others to use—are out of scope. The variation within the types of articles we publish in MEE is endless but still bounded. If you think your manuscript might be a good fit but you are not sure, please ask us before you submit it. Every year, during the Annual Meeting of the British Ecological Society, the senior editors of all the BES journals participate in “speed review” sessions where we provide feedback to undergraduate and graduate students, postdocs, and senior researchers on their works-in-progress and discuss which journals might be appropriate outlets for their manuscripts. If you cannot make it to the UK in mid-December though, we will always give the same amount of feedback via email. Just send the title, abstract and a brief note about why you think it is appropriate for MEE to our editorial office. We are always looking for the next methodological breakthrough and you might have it! AME wrote the paper. The author declares no conflicts of interest. The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210X.14232. No data needed or used in this editorial.

Plant phenology has been shifting dramatically in response to climate change, a shift that may have significant and widespread ecological consequences. Of particular concern are tropical biomes, which represent the most biodiverse and imperiled regions of the world. However, compared to temperate floras, we know little about phenological responses of tropical plants because long-term observational datasets from the tropics are sparse. Herbarium specimens have greatly increased our phenological knowledge in temperate regions, but similar data have been underutilized in the tropics and their suitability for this purpose has not been broadly validated. Here, we compare phenological estimates derived from field observational data (i.e., plot surveys) and herbarium specimens at various spatial and taxonomic scales to determine whether specimens can provide accurate estimations of reproductive timing and its spatial variation. Here we demonstrate that phenological estimates from field observations and herbarium specimens coincide well. Fewer than 5% of the species exhibited significant differences between flowering periods inferred from field observations versus specimens regardless of spatial aggregation. In contrast to studies based on field records, herbarium specimens sampled much larger geographic and climatic ranges, as has been documented previously for temperate plants, and effectively captured phenological responses across varied environments. Herbarium specimens are verified to be a vital resource for closing the gap in our phenological knowledge of tropical systems. Tropical plant reproductive phenology inferred from herbarium records are widely congruent with field observations, suggesting that they can (and should) be used to investigate phenological variation and their associated environmental cues more broadly across tropical biomes.