Jacob C. Douma

A fully traits-based approach to modeling global vegetation distribution

Significance Models on vegetation dynamics are indispensable for our understanding of climate change impacts. These models contain variables describing vegetation attributes, so-called traits. However, the direct impacts of trait variation on global vegetation distribution are unknown. We derived global trait maps based on information on environmental drivers. Subsequently, we characterized nine globally representative vegetation types based on their trait combinations and could make valid predictions of their global occurrence probabilities based on trait maps. This study provides a proof of concept for the link between plant traits and vegetation types, stimulating enhanced application of trait-based approaches in vegetation modeling. We envision that our approach, our observation-driven trait maps, and vegetation maps may inspire a new generation of powerful traits-based vegetation models. Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs.

Bordetella pertussis: an underreported pathogen in pediatric respiratory infections, a prospective cohort study

BackgroundThe incidence of pertussis has been increasing worldwide. In the Netherlands, the seroprevalence has risen higher than the reported cases, suggesting that laboratory tests for pertussis are considered infrequently and that even more pertussis cases are missed. The objective of our study was to determine the frequency of pertussis in clinically unsuspect cases compared to suspect cases with the intention of finding clinical predictors.MethodsThe present prospective cohort study was part of a controlled clinical trial evaluating the impact of molecular diagnostics on clinical decision making in pediatric respiratory infections, performed during 2 winter seasons. For this study, in the first season pertussis was only tested in case of clinical suspicion, in the second season, pertussis was also tested without clinical suspicion. Multivariate and univariate analysis were performed using SPSS 18 and Statistical software ‘R’.ResultsIn the two seasons respectively 22/209 (10,5%) and 49/373 (13,1%) cases were clinically suspected of pertussis. Bordetella pertussis was detected by real time RT-PCR in respectively 2/22 (9,1%) and 7/49 (14,3%) cases. In the second season an additional 7 cases of pertussis were found in clinically unsuspected cases (7/257 = 2,7%). These additional cases didn’t differ in clinical presentation from children without a positive test for pertussis with respect to respiratory symptoms.ConclusionsPertussis in children sometimes mimics viral respiratory tract infections. If pertussis diagnostics are based on clinical suspicion alone, about 1 in 5 cases (19%) is missed. Despite widely accepted clinical criteria, paroxysmal cough is not a good predictor of pertussis. To prevent spreading, physicians should include B. pertussis in routine diagnostics in respiratory tract infections.

A risk categorisation and analysis of the geographic and temporal dynamics of the European import of plants for planting

The international trade in plants for planting (P4Ps) is a major pathway for the introduction of plant pests. The global trade in P4Ps is both voluminous and highly diverse, but there is little detailed knowledge about its diversity and dynamics. This makes it difficult to assess the risks associated with this trade and to prioritise high-risk commodities (genus-origin combinations) for detailed inspection or regulation. Using the ISEFOR database, this paper describes the diversity and dynamics of P4P imports into the EU, based on genus-level data for lots imported into fourteen Member States that provided this data for different periods between 2005 and 2014, totalling over 30Bn plants and over 7500 commodities. There was great variety, as well as complementarity, in terms of the imported genera, origins and commodities among the countries. Two-thirds of the imported commodities changed every year. Based on the 10-year data from the Netherlands, the greatest importer of live plants in the dataset, we developed a risk categorisation approach for prioritising the highest risk commodities, based on risk associated information concerning the imported genus and the history of trade with respect to the exporting countries, genera and type of plant material traded. Application of this risk categorisation led to the identification of a modest number of commodities that represent elevated risk, to which more inspection resources can be allocated while lower-risk commodities could be subject to less-intensive phytosanitary inspections.

Semi-natural habitats support biological control, pollination and soil conservation in Europe. A review

Semi-natural habitats are integral to most agricultural areas and have the potential to support ecosystem services, especially biological control and pollination by supplying resources for the invertebrates providing these services and for soil conservation by preventing erosion and run-off. Some habitats are supported through agri-environment scheme funding in the European Union, but their value for ecosystem service delivery has been questioned. An improved understanding of previous research approaches and outcomes will contribute to the development of more sustainable farming systems, improve experimental designs and highlight knowledge gaps especially for funders and researchers. Here we compiled a systematic map to allow for the first time a review of the quantity of evidence collected in Europe that semi-natural habitats support biological control, pollination and soil conservation. A literature search selected 2252 publications, and, following review, 270 met the inclusion criteria and were entered into the database. Most publications were of pest control (143 publications) with less on pollination (78 publications) or soil-related aspects (31). For pest control and pollination, most publications reported a positive effect of semi-natural habitats. There were weaknesses in the evidence base though because of bias in study location and the crops, whilst metrics (e.g. yield) valued by end users were seldom measured. Hedgerows, woodland and grassland were the most heavily investigated semi-natural habitats, and the wider landscape composition was often considered. Study designs varied considerably yet only 24% included controls or involved manipulation of semi-natural habitats. Service providers were commonly measured and used as a surrogate for ecosystem service delivery. Key messages for policymakers and funders are that they should encourage research that includes more metrics required by end users, be prepared to fund longer-term studies (61% were of only 1-year duration) and investigate the role of soils within semi-natural habitats in delivering ecosystem services.

When does it pay off to prime for defense? A modeling analysis

Summary Plants can prepare for future herbivore attack through a process called priming. Primed plants respond more strongly and/or faster to insect attack succeeding the priming event than nonprimed plants, while the energetic costs of priming are relatively low. To better understand the evolution of priming, we developed a simulation model, partly parameterized for Brassica nigra plants, to explore how the fitness benefits of priming change when plants are grown in different biotic environments. Model simulations showed that herbivore dynamics (arrival probability, arrival time, and feeding rate) affect the optimal duration, the optimal investment and the fitness benefits of priming. Competition for light increases the indirect costs of priming, but may also result in a larger payoff when the nonprimed plant experiences substantial leaf losses. This modeling approach identified some important knowledge gaps: herbivore arrival rates on individual plants are rarely reported but they shape the optimal duration of priming, and it would pay off if the likelihood, severity and timing of the attack could be discerned from the priming cue, but it is unknown if plants can do so. In addition, the model generated some testable predictions, for example that the sensitivity to the priming cue decreases with plant age.

Grain legume cultivation and children’s dietary diversity in smallholder farming households in rural Ghana and Kenya

Boosting smallholder food production can potentially improve children’s nutrition in rural Sub-Saharan Africa through a production-own consumption pathway and an income-food purchase pathway. Rigorously designed studies are needed to provide evidence for nutrition impact, but are often difficult to implement in agricultural projects. Within the framework of a large agricultural development project supporting legume production (N2Africa), we studied the potential to improve children’s dietary diversity by comparing N2Africa and non-N2Africa households in a cross-sectional quasi-experimental design, followed by structural equation modelling (SEM) and focus group discussions in rural Ghana and Kenya. Comparing N2Africa and non-N2Africa households, we found that participating in N2Africa was not associated with improved dietary diversity of children. However, for soybean, SEM indicated a relatively good fit to the posteriori model in Kenya but not in Ghana, and in Kenya only the production-own consumption pathway was fully supported, with no effect through the income-food purchase pathway. Results are possibly related to differences in the food environment between the two countries, related to attribution of positive characteristics to soybean, the variety of local soybean-based dishes, being a new crop or not, women’s involvement in soybean cultivation, the presence of markets, and being treated as a food or cash crop. These findings confirm the importance of the food environment for translation of enhanced crop production into improved human nutrition. This study also shows that in a situation where rigorous study designs cannot be implemented, SEM is a useful option to analyse whether agriculture projects have the potential to improve nutrition.

Development of a pathway model to assess the exposure of European pine trees to pine wood nematode via the trade of wood

Pine wood nematode (PWN), Bursaphelenchus xylophilus, is a threat for pine species (Pinus spp.) throughout the world. The nematode is native to North America, and invaded Japan, China, Korea, and Taiwan, and more recently Portugal and Spain. PWN enters new areas through trade in wood products. Once established, eradication is not practically feasible. Therefore, preventing entry of PWN into new areas is crucial. Entry risk analysis can assist in targeting management to reduce the probability of entry. Assessing the entry of PWN is challenging due to the complexity of the wood trade and the wood processing chain. In this paper, we develop a pathway model that describes the wood trade and wood processing chain to determine the structure of the entry process. We consider entry of PWN through imported coniferous wood from China, a possible origin of Portuguese populations, to Europe. We show that exposure increased over years due to an increase in imports of sawn wood. From 2000 to 2012, Europe received an estimated 84 PWN propagules from China, 88% of which arose from imported sawn wood and 12% from round wood. The region in Portugal where the PWN was first reported is among those with the highest PWN transfer per unit of imported wood due to a high host cover and vector activity. An estimated 62% of PWN is expected to enter in countries where PWN is not expected to cause the wilt of pine trees because of low summer temperatures (e.g., Belgium, Sweden, Norway). In these countries, PWN is not easily detected, and such countries can thus serve as potential reservoirs of PWN. The model identifies ports and regions with high exposure, which helps targeting monitoring and surveillance, even in areas where wilt disease is not expected to occur. In addition, we show that exposure is most efficiently reduced by additional treatments in the country of origin, and/or import wood from PWN-free zones. Pathway modelling assists plant health managers in analyzing risks along the pathway and planning measures for enhancing biosecurity.