Fergus Chadwick

Ecology has undergone a profound methodological transformation through the rapid growth in new technologies, data volume, statistical innovation and analytical sophistication. These developments create unprecedented opportunities for novel ecological insights, but have also magnified longstanding concerns about quantitative training, which persist despite more than a century of debate and repeated calls for reform. Here, we posit why, despite enduring and widespread agreement on educational goals, progress in quantitative ecology education has remained limited. We argue that limited progress reflects a mismatch between widely supported reforms and the conditions needed to implement them at scale. While discussions of quantitative education have often focused on curricular content—what ecologists should learn—progress is also shaped by pedagogical, interpersonal, institutional and disciplinary factors. We discuss these interconnected components and argue that their combined influence helps explain the inertia that continues to characterise quantitative ecology education. Building on this diagnosis, we offer an integrated vision for quantitative ecology education. We argue that durable quantitative literacy is best developed through inclusive, context‐rich and people‐centred pedagogies that address barriers to learning, support diverse learners and ensure that procedural competence is grounded in strong conceptual understanding. We further advocate for coordinated action from disciplinary leaders, stronger institutional support for educators and learners, recognition of heterogeneous cohorts as a pedagogical strength, greater interdisciplinary collaboration between ecologists and quantitative specialists, and a shift from expectations of universal fluency towards a model of distributed expertise underpinned by foundational quantitative literacy. As ecology evolves, the need for quantitative literacy is greater than ever. Progress will depend not simply on identifying what should be taught and how, but on addressing the broader conditions that constrain the pace and scale of implementation of widely supported educational aspirations. We argue that reframing persistent challenges as interconnected problems of implementation, rather than aspiration, will help in overcoming a century of inertia in quantitative ecology education.

1 Citizen science data often contain high levels of species misclassification that can bias inference and conservation decisions. Current approaches to address mislabelling rely on expert taxonomists validating every record. This approach makes intensive use of a scarce resource and reduces the role of the citizen scientist. Species, however, are not confused at random. If two species appear more similar, it is probable they will be more easily confused than two highly distinctive species. Identification guides are intended to use these patterns to aid correct classification, but misclassifications still occur due to user-error and imperfect guidebook design. Statistical models should be able to exploit this non-randomness to learn confusion patterns from small validation datasets provided by expert taxonomists, yielding a much-needed reduction in expert workload. Here, we use a variety of Bayesian hierarchical models to probabilistically classify species based on the species-label provided by the citizen scientist. We also explore the utility of guidebooks provided by the citizen science schemes as a prior for species similarity, and hence draw conclusions for their future improvement. We find that the species-label assigned to a record by a citizen scientist, even when incorrect, contains useful information about the true species-identity. The citizen scientists correctly identify the species in around 58% of records. Using models trained on only 10% of these records (validated by experts), we can correctly predict species-identity for 69 (90%CI: 64-73)% of records when the guidebook is used, vs 64 (58-69)% for models that do not use the guidebook. The fact that misclassifications can be predicted systematically indicates that improvements could be made to the guidebook to reduce misclassification. By using Bayesian, hierarchical models we can greatly reduce the workload for experts by providing a probabilistic correction to citizen science records, rather than requiring manual review. This is increasingly important as the number of citizen science schemes grows and the relative number of taxonomists shrinks. By learning confusion patterns statistically, we open up future avenues of research to identify what causes these confusions and how to better address them.

Diagnostics for COVID-19 detection are limited in many settings. Syndromic surveillance is often the only means to identify cases but lacks specificity. Rapid antigen testing is inexpensive and easy-to-deploy but can lack sensitivity. We examine how combining these approaches can improve surveillance for guiding interventions in low-income communities in Dhaka, Bangladesh. Rapid-antigen-testing with PCR validation was performed on 1172 symptomatically-identified individuals in their homes. Statistical models were fitted to predict PCR-status using rapid-antigen-test results, syndromic data, and their combination. Under contrasting epidemiological scenarios, the models’ predictive and classification performance was evaluated. Models combining rapid-antigen-testing and syndromic data yielded equal-to-better performance to rapid-antigen-test-only models across all scenarios with their best performance in the epidemic growth scenario. These results show that drawing on complementary strengths across rapid diagnostics, improves COVID-19 detection, and reduces false-positive and -negative diagnoses to match local requirements; improvements achievable without additional expense, or changes for patients or practitioners.

Diagnostics for COVID-19 detection are limited in many settings. Syndromic surveillance is often the only means to identify cases, but lacks specificity. Rapid antigen testing is inexpensive and easy-to-deploy but concerns remain about sensitivity. We examine how combining these approaches can improve surveillance for guiding interventions in low-income communities in Dhaka, Bangladesh. Rapid-antigen-tests and PCR validation was performed on 1172 symptomatically-identified individuals at home. Statistical models were fit to predict PCR status using rapid-antigen-test results, syndromic data, and their combination. Model predictive and classification performance was examined under contrasting epidemiological scenarios to evaluate their potential for improving diagnoses. Models combining rapid-antigen-test and syndromic data yielded equal-to-better performance to rapid-antigen-test-only models across all scenarios. These results show that drawing on complementary strengths across two rapid diagnostics, improves COVID-19 detection, and reduces false-positive and -negative diagnoses to match local requirements; improvements achievable without additional expense, or changes for patients or practitioners.

Context: There have been dramatic global declines in pollinating insects. A common land management intervention to support wild pollinators is to plant non-crop flowering plants (‘pollinator planting’). However, there are limited data on which species or spatial arrangement of planting provide maximum benefit to wild pollinators.Objectives: Here we investigate which flowering species and locations are visited by free-foragingBombus terrestris(buff-tailed bumblebees) in species-rich semi-natural grassland and woodland.Methods: Two study nests of buff-tailed bumblebees were established in Wytham Woods, UK. Pollen analogue pigments were sprayed on open flowers in the study area over a period of two months, with unique colours used to identify separate sections of the study area. Pollen load analysis was used to identify forage species and foraging locations.Results: Bumblebees showed low flower constancy, visiting five flower species per trip on average, and as a group the sampled bumblebees visited 36 of the 49 plant species identified in study area surveys. Many individuals foraged in multiple, spatially-discrete locations during single trips.Conclusions: The positive relationship between floral diversity and pollen load species diversity, and the positive relationship between site floral diversity and frequency of visitation, suggest behavioural strategies that maximize the diversity of flower species visited, in line with theenergetic costs and benefits hypothesis. This supports recommendations for pollinator plantings with high species diversity, potentially spread across many small forage areas across the landscape.

Background: COVID-19 is responsible for increasing deaths globally. As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some speculate that YLL are low. We aim to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs, using the limited data available early in the pandemic. Methods: We first estimated YLL from COVID-19 using WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs in a Bayesian model to estimate likely combinations of LTCs among people dying with COVID-19. We used routine UK healthcare data from Scotland and Wales to estimate life expectancy based on age/sex/these combinations of LTCs using Gompertz models from which we then estimate YLL. Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (11.6 and 9.4 years for men and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6). Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data (including LTC type, severity, and potential confounders such as socioeconomic-deprivation and care-home status) is needed to optimise YLL estimates for specific populations, and to understand the global burden of COVID-19, and guide policy-making and interventions.

Background: COVID-19 is responsible for increasing deaths globally. Estimates focused on numbers of deaths, do not quantify potential years of life lost (YLL) through COVID-19. As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some speculate that YLL are low. We aim to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs. Methods: We first estimated YLL from COVID-19 using WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs inform a Bayesian model for likely combinations of LTCs among people dying with COVID-19. From these, we used routine UK healthcare data from Scotland and Wales to estimate life expectancy based on age/sex/ combinations of LTCs using Gompertz models. We then calculated YLL based on age, sex, type of LTCs and multimorbidity count. Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (11.6 and 9.4 years for man and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6). Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data on LTCs is needed to better understand and quantify the global burden of COVID-19 and to guide policy-making and interventions.

Background: The COVID-19 pandemic is responsible for increasing deaths globally. Most estimates have focused on numbers of deaths, with little direct quantification of years of life lost (YLL) through COVID-19. As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some have speculated that YLL are low. We aim to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs. Methods: We first estimated YLL from COVID-19 using standard WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs to model likely combinations of LTCs among people dying with COVID-19. From these, we used routine UK healthcare data to estimate life expectancy based on age/sex/different combinations of LTCs. We then calculated YLL based on age, sex and type of LTCs and multimorbidity count. Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (13 and 11 years for men and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6). Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data on LTCs is needed to better understand and quantify the global burden of COVID-19 and to guide policy-making and interventions.