In legume-Rhizobium symbioses, specialised soil bacteria fix atmospheric nitrogen in return for carbon. However, ineffective strains can arise, making discrimination essential. Discrimination can occur via partner choice, where legumes prevent ineffective strains from entering, or via sanctioning, where plants provide fewer resources. Several studies have inferred that legumes exercise partner choice, but the rhizobia compared were not otherwise isogenic. To test when and how plants discriminate ineffective strains we developed sets of fixing and non-fixing strains that differed only in the expression of nifH – essential for nitrogen fixation – and could be visualised using marker genes. We show that the plant is unable to select against the non-fixing strain at the point of entry, but that non-fixing nodules are sanctioned. We also used the technique to characterise mixed nodules (containing both a fixing and a non-fixing strain), whose frequency could be predicted using a simple diffusion model. We discuss that sanctioning is likely to evolve in preference to partner choice in any symbiosis where partner quality cannot be adequately assessed until goods or services are actively exchanged.

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Invasive species threaten global biodiversity, food security and ecosystem function.Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1,739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75-0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling. K E Y W O R D S agriculture, biogeography, food security, invasive species, observational bias, pest risk analysis, species distribution model

Invasive species threaten global biodiversity, food security and ecosystem function. Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1,739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75–0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling.

Diversifying planted forests by increasing genetic and species diversity is often promoted as a method to improve forest resilience to climate change and reduce pest and pathogen damage. In this study, we used a young tree diversity experiment replicated at two sites in the UK to study the impacts of tree diversity and tree provenance (geographic origin) on the oak ( Quercus robur ) insect herbivore community and a specialist biotrophic pathogen, oak powdery mildew. Local UK, French, and Italian provenances were planted in monocultures, provenance mixtures, and species mixes, allowing us to test whether: (a) local and nonlocal provenances differ in their insect herbivore and pathogen communities, and (b) admixing trees leads to associational effects on insect herbivore and pathogen damage. Tree diversity had variable impacts on foliar organisms across sites and years, suggesting that diversity effects can be highly dependent on environmental context. Provenance identity impacted upon both herbivores and powdery mildew, but we did not find consistent support for the local adaptation hypothesis for any group of organisms studied. Independent of provenance, we found tree vigor traits (shoot length, tree height) and tree apparency (the height of focal trees relative to their surroundings) were consistent positive predictors of powdery mildew and insect herbivory. Synthesis . Our results have implications for understanding the complex interplay between tree identity and diversity in determining pest damage, and show that tree traits, partially influenced by tree genotype, can be important drivers of tree pest and pathogen loads.