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The degree of interdisciplinarity in research proposals negatively correlates with funding success across a wide range of research fields. The cost of interdisciplinarity In the recent past, governments and funding bodies have been keen to promote the merits of interdisciplinary research. But there is a widely held belief that such research faces higher barriers when it comes to applying for funding. Lindell Bromham et al . have mined data from the Australian Research Council to establish whether this gut feeling is based on reality. It is. They find that the degree of interdisciplinarity correlates negatively with funding success, irrespective of the research field. The authors go on to develop a metric — the interdisciplinary distance or IDD — that can be used to single out grant submissions that might be prone to this type of bias, so that their evaluation can be addressed proactively. Interdisciplinary research is widely considered a hothouse for innovation, and the only plausible approach to complex problems such as climate change 1 , 2 . One barrier to interdisciplinary research is the widespread perception that interdisciplinary projects are less likely to be funded than those with a narrower focus 3 , 4 . However, this commonly held belief has been difficult to evaluate objectively, partly because of lack of a comparable, quantitative measure of degree of interdisciplinarity that can be applied to funding application data 1 . Here we compare the degree to which research proposals span disparate fields by using a biodiversity metric that captures the relative representation of different fields (balance) and their degree of difference (disparity). The Australian Research Council’s Discovery Programme provides an ideal test case, because a single annual nationwide competitive grants scheme covers fundamental research in all disciplines, including arts, humanities and sciences. Using data on all 18,476 proposals submitted to the scheme over 5 consecutive years, including successful and unsuccessful applications, we show that the greater the degree of interdisciplinarity, the lower the probability of being funded. The negative impact of interdisciplinarity is significant even when number of collaborators, primary research field and type of institution are taken into account. This is the first broad-scale quantitative assessment of success rates of interdisciplinary research proposals. The interdisciplinary distance metric allows efficient evaluation of trends in research funding, and could be used to identify proposals that require assessment strategies appropriate to interdisciplinary research 5 .

The changes in phylogenetic composition and structure of communities during succession following disturbance can give us insights into the forces that are shaping communities over time. In abandoned agricultural fields, community composition changes rapidly when a field is plowed, and is thought to reflect a relaxation of competition due to the elimination of dominant species which take time to re-establish. Competition can drive phylogenetic overdispersion, due to phylogenetic conservation of ‘niche’ traits that allow species to partition resources. Therefore, undisturbed old field communities should exhibit higher phylogenetic dispersion than recently disturbed systems, which should be relatively ‘clustered’ with respect to phylogenetic relationships. Several measures of phylogenetic structure between plant communities were measured in recently plowed areas and nearby ‘undisturbed’ sites. There was no difference in the absolute values of these measures between disturbed and ‘undisturbed’ sites. However, there was a difference in the ‘expected’ phylogenetic structure between habitats, leading to significantly lower than expected phylogenetic diversity in disturbed plots, and no difference from random expectation in ‘undisturbed’ plots. This suggests that plant species characteristic of each habitat are fairly evenly distributed on the shared species pool phylogeny, but that once the initial sorting of species into the two habitat types has occurred, the processes operating on them affect each habitat differently. These results were consistent with an analysis of correlation between phylogenetic distance and co-occurrence indices of species pairs in the two habitat types. This study supports the notion that disturbed plots are more clustered than expected, rather than ‘undisturbed’ plots being more overdispersed, suggesting that disturbed plant communities are being more strongly influenced by environmental filtering of conserved niche traits.

The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages.

Data on the three dimensional shape of organismal morphology is becoming increasingly available, and forms part of a new revolution in high-throughput phenomics that promises to help understand ecological and evolutionary processes that influence phenotypes at unprecedented scales. However, in order to meet the potential of this revolution we need new data analysis tools to deal with the complexity and heterogeneity of large-scale phenotypic data such as 3D shapes. In this study we explore the potential of generative Artificial Intelligence to help organize and extract meaning from complex 3D data. Specifically, we train a deep representational learning method known as DeepSDF on a dataset of 3D scans of the bills of 2,020 bird species. The model is designed to learn a continuous vector representation of 3D shapes, along with a ’decoder’ function, that allows the transformation from this vector space to the original 3D morphological space. We find that approach successfully learns coherent representations: particular directions in latent space are associated with discernible morphological meaning (such as elongation, flattening, etc.). More importantly, learned latent vectors have ecological meaning as shown by their ability to predict the trophic niche of the bird each bill belongs to with a high degree of accuracy. Unlike existing 3D morphometric techniques, this method has very little requirements for human supervised tasks such as landmark placement, increasing it accessibility to labs with fewer labour resources. It has fewer strong assumptions than alternative dimension reduction techniques such as PCA. Once trained, 3D morphology predictions can be made from latent vectors very computationally cheaply. The trained model has been made publicly available and can be used by the community, including for finetuning on new data, representing an early step toward developing shared, reusable AI models for analyzing organismal morphology.

Ecosystem stability in variable environments depends on the diversity of form and function of the constituent species. Species phenotypes and ecologies are the product of evolution, and the evolutionary history represented by co‐occurring species has been shown to be an important predictor of ecosystem function. If phylogenetic distance is a surrogate for ecological differences, then greater evolutionary diversity should buffer ecosystems against environmental variation and result in greater ecosystem stability. We calculated both abundance‐weighted and unweighted phylogenetic measures of plant community diversity for a long‐term biodiversity–ecosystem function experiment at Cedar Creek, Minnesota, USA. We calculated a detrended measure of stability in aboveground biomass production in experimental plots and showed that phylogenetic relatedness explained variation in stability. Our results indicate that communities where species are evenly and distantly related to one another are more stable compared to communities where phylogenetic relationships are more clumped. This result could be explained by a phylogenetic sampling effect, where some lineages show greater stability in productivity compared to other lineages, and greater evolutionary distances reduce the chance of sampling only unstable groups. However, we failed to find evidence for similar stabilities among closely related species. Alternatively, we found evidence that plot biomass variance declined with increasing phylogenetic distances, and greater evolutionary distances may represent species that are ecologically different (phylogenetic complementarity). Accounting for evolutionary relationships can reveal how diversity in form and function may affect stability.

Language diversity is under threat. While each language is subject to specific social, demographic and political pressures, there may also be common threatening processes. We use an analysis of 6,511 spoken languages with 51 predictor variables spanning aspects of population, documentation, legal recognition, education policy, socioeconomic indicators and environmental features to show that, counter to common perception, contact with other languages per se is not a driver of language loss. However, greater road density, which may encourage population movement, is associated with increased endangerment. Higher average years of schooling is also associated with greater endangerment, evidence that formal education can contribute to loss of language diversity. Without intervention, language loss could triple within 40 years, with at least one language lost per month. To avoid the loss of over 1,500 languages by the end of the century, urgent investment is needed in language documentation, bilingual education programmes and other community-based programmes. Using a global analysis of 6,511 spoken languages with 51 predictor variables spanning aspects of population, documentation, legal recognition, education policy, socioeconomic indicators and environmental features, the authors identify predictors of current and future language endangerment and loss.

Long-standing ecological theory proposes that diverse communities of plants should experience a decrease in herbivory. Yet previous empirical examinations of this hypothesis have revealed that plant species richness increases herbivory in just as many systems as it decreases it. In this study, I ask whether more insight into the role of plant diversity in promoting or suppressing herbivory can be gained by incorporating information about the evolutionary history of species in a community. In an old field system in southern Ontario, I surveyed communities of plants and measured levels of leaf damage on 27 species in 38 plots. I calculated a measure of phylogenetic diversity (PSE) that encapsulates information about the amount of evolutionary history represented in each of the plots and looked for a relationship between levels of herbivory and both species richness and phylogenetic diversity using a generalized linear mixed model (GLMM) that could account for variation in herbivory levels between species. I found that species richness was positively associated with herbivore damage at the plot-level, in keeping with the results from several other recent studies on this question. On the other hand, phylogenetic diversity was associated with decreased herbivory. Importantly, there was also an interaction between species richness and phylogenetic diversity, such that plots with the highest levels of herbivory were plots which had many species but only if those species tended to be closely related to one another. I propose that these results are the consequence of interactions with herbivores whose diets are phylogenetically specialized (for which I introduce the term cladophage), and how phylogenetic diversity may alter their realized host ranges. These results suggest that incorporating a phylogenetic perspective can add valuable additional insight into the role of plant diversity in explaining or predicting levels of herbivory at a whole-community scale.

Microbial symbiosis is integral to plant growth and reproduction, but its contribution to global patterns of plant distribution is unknown. Legumes ( Fabaceae ) are a diverse and widely distributed plant family largely dependent on symbiosis with nitrogen-fixing rhizobia, which are acquired from soil after germination. This dependency is predicted to limit establishment in new geographic areas, owing to a disruption of compatible host-symbiont associations. Here we compare non-native establishment patterns of symbiotic and non-symbiotic legumes across over 3,500 species, covering multiple independent gains and losses of rhizobial symbiosis. We find that symbiotic legume species have spread to fewer non-native regions compared to non-symbiotic legumes, providing strong support for the hypothesis that lack of suitable symbionts or environmental conditions required for effective nitrogen-fixation are driving these global introduction patterns. These results highlight the importance of mutualisms in predicting non-native species establishment and the potential impacts of microbial biogeography on global plant distributions. Symbiosis with nitrogen-fixing bacteria (rhizobia) aids the growth of many legume species, but may also restrict their ability to colonize new regions lacking suitable rhizobia. Here, the authors show that symbiotic legumes are indeed less likely to become established in new regions than their non-symbiotic relatives.

Plant phenology plays a fundamental role in shaping ecosystems, and global change‐induced shifts in phenology have cascading impacts on species interactions and ecosystem structure and function. Detailed, high‐quality observations of when plants undergo seasonal transitions such as leaf‐out, flowering and fruiting are critical for tracking causes and consequences of phenology shifts, but these data are often sparse and biased globally. These data gaps limit broader generalizations and forecasting improvements in the face of continuing disturbance. One solution to closing such gaps is to document phenology on field images taken by public participants. iNaturalist, in particular, provides global‐scale research‐grade data and is expanding rapidly. Here we utilize over 53 million field images of plants and millions of human annotations from iNaturalist—data spanning all angiosperms and drawn from across the globe—to train a computer vision model (PhenoVision) to detect the presence of fruits and flowers. PhenoVision utilizes a vision transformer architecture pretrained with a masked autoencoder to improve classification success, and it achieves high accuracy on held‐out test images for flower (98.5%) and fruit presence (95%), as well as a high level of agreement with an expert annotator (98.6% for flowers and 90.4% for fruits). Key to producing research‐ready phenology data is post‐calibration tuning and validation focused on reducing noise inherent in field photographs, and maximizing the true positive rate. We also develop a standardized set of quality metrics and metadata so that results can be used effectively by the community. Finally, we showcase how this effort vastly increases phenology data coverage, including regions of the globe where data have been limited before. Our end products are tuned models, new data resources and an application streamlining discovery and use of those data for the broader research and management community. We close by discussing next steps, including automating phenology annotations, adding new phenology targets, for example leaf phenology, and further integration with other resources to form a global central database integrating all in situ plant phenology resources.