John Paul Schmidt

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities.

El orden Carnivora incluye más de 300 especies que varían en tamaño en muchos órdenes de magnitud y habitan en todos los biomas principales, desde las selvas tropicales hasta los mares polares. La gran diversidad de parásitos carnívoros representa una fuente de posibles enfermedades emergentes en humanos. El riesgo zoonótico de este grupo puede deberse en parte, a una diversidad funcional excepcionalmente alta de las especies hospedantes en cuanto a características conductuales, fisiológicas y ecológicas. Revisamos los patrones macroecológicos globales de los parásitos zoonóticos dentro de los carnívoros y exploramos las características de las especies que sirven como anfitriones de los parásitos zoonóticos. Sintetizamos la investigación teórica y empírica y sugerimos trabajos futuros sobre el papel de los carnívoros como multiplicadores bióticos, reguladores y centinelas de enfermedades zoonóticas como fronteras de investigación oportunas.

Zoonotic disease outbreaks are an important threat to human health and numerous drivers have been recognized as contributing to their increasing frequency. Identifying and quantifying relationships between drivers of zoonotic disease outbreaks and outbreak severity is critical to developing targeted zoonotic disease surveillance and outbreak prevention strategies. However, quantitative studies of outbreak drivers on a global scale are lacking. Attributes of countries such as press freedom, surveillance capabilities and latitude also bias global outbreak data. To illustrate these issues, we review the characteristics of the 100 largest outbreaks in a global dataset (n = 4463 bacterial and viral zoonotic outbreaks), and compare them with 200 randomly chosen background controls. Large outbreaks tended to have more drivers than background outbreaks and were related to large-scale environmental and demographic factors such as changes in vector abundance, human population density, unusual weather conditions and water contamination. Pathogens of large outbreaks were more likely to be viral and vector-borne than background outbreaks. Overall, our case study shows that the characteristics of large zoonotic outbreaks with thousands to millions of cases differ consistently from those of more typical outbreaks. We also discuss the limitations of our work, hoping to pave the way for more comprehensive future studies. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

Due to expanding global trade and movement of people, new plant species are establishing in exotic ranges at increasing rates while the number of native species facing extinction from multiple threats grows. Yet, how species losses and gains globally may, together, be linked to traits and macroevolutionary processes is poorly understood. Here, we show that, adjusting for diversification rate and clade age, the proportion of threatened species across flowering plant families is negatively related to the proportion of naturalised species per family. Moreover, naturalisation is positively associated with range size, short generation time, autonomous seed production and interspecific hybridisation, but negatively with age and diversification, whereas threat is negatively associated with range size and hybridisation, and positively with biotic pollination, age and diversification rate. That we find such a pronounced signature of naturalisation and threat across plant families suggests that both trait syndromes have coexisted over deep evolutionary time and counter to intuition, that neither strategy is necessarily superior to the other over long evolutionary timespans.

Due to expanding global trade and movement, new plant species are establishing in exotic ranges at increasing rates while the number of native species facing extinction from multiple threats grows. Yet, how species losses and gains globally may together be linked to traits and macroevolutionary processes is poorly understood. Here we show that, adjusting for diversification rate and age, the proportion of threatened species across flowering plant families is negatively related to the proportion of naturalized species. Moreover, naturalization is positively associated with climate variability, short generation time, autonomous seed production, and interspecific hybridization, but negatively with age and diversification; whereas threat is negatively associated with climate variability and hybridization, and positively with biotic pollination, age and diversification. Such a pronounced signature of naturalization and threat across plant families suggests that both trait syndromes have coexisted over deep evolutionary time and that neither strategy is necessarily superior to the other.

Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent‐borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining‐modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.

Aim: Parasites are a major component of global ecosystems, yet spatial variation in parasite diversity is poorly known, largely because their occurrence data are limited and thus difficult to interpret. Using a recently compiled database of parasite occurrences, we compare different models which we use to infer parasite geographic ranges and parasite species richness across the globe.Innovation: To date, most studies exploring spatial patterns of parasite diversity assumed, with little validation, that the geographic range of a parasite species can be represented by the collective geographic range of its host species. Our study compares this assumption with a suite of other methods to infer parasite distribution from parasite occurrence data (e.g., based on data density, ecoregions and climatic conditions). We highlight diversity hotspots identified by the various methods and compare the effects of sampling intensities in different regions, a crucial factor determining observed parasite diversity.Main conclusions: The type of model used to infer parasite distributions affects estimates of both total species richness and spatial patterns of hotspots of parasite richness. Overall, the models based on reported occurrences share similar areas of high parasite richness that tend to be biased towards areas of high sampling effort. In contrast, the model based on host distributions showed hotspots of parasite diversity that are biased towards areas of high host species richness. Accounting for sampling effort could only help to reconcile the outcome from the different models in some regions. Further, the non‐saturated species accumulation curves even for the best studied regions of the world such as Europe and North America serve as a call for further sampling effort and development of effective analytic tools that can provide robust accounts of global parasite diversity.

Much of the basic ecology of Ebolavirus remains unresolved despite accumulating disease outbreaks, viral strains and evidence of animal hosts. Because human Ebolavirus epidemics have been linked to contact with wild mammals other than bats, traits shared by species that have been infected by Ebolavirus and their phylogenetic distribution could suggest ecological mechanisms contributing to human Ebolavirus spillovers. We compiled data on Ebolavirus exposure in mammals and corresponding data on life-history traits, movement, and diet, and used boosted regression trees (BRT) to identify predictors of exposure and infection for 119 species (hereafter hosts). Mapping the phylogenetic distribution of presumptive Ebolavirus hosts reveals that they are scattered across several distinct mammal clades, but concentrated among Old World fruit bats, primates and artiodactyls. While sampling effort was the most important predictor, explaining nearly as much of the variation among hosts as traits, BRT models distinguished hosts from all other species with greater than 97% accuracy, and revealed probable Ebolavirus hosts as large-bodied, frugivorous, and with slow life histories. Provisionally, results suggest that some insectivorous bat genera, Old World monkeys and forest antelopes should receive priority in Ebolavirus survey efforts. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.