Shedding new light on the origin and spread of the brinjal eggplant (Solanum melongena L.; Solanaceae) and its wild relatives

• While brinjal eggplant (Solanum melongena L.) is the second most important solanaceaous vegetable crop, we lack firm knowledge of its evolutionary relationships. This in turn limits efficient use of crop wild relatives in eggplant improvement. Here, we examine the hypothesis of linear step-wise expansion of the eggplant group from Africa to Asia. • We use museum collections to generate nuclear and full-plastome data for all species of the eggplant clade. We combine a phylogenomic approach with distribution data to infer a biogeographic scenario for the clade. • The eggplant clade has Pleistocene origins in northern Africa. Dispersions to tropical Asia gave rise to Solanum insanum, the wild progenitor of the eggplant, and to Africa distinct lineages of widespread and southern-African species. Results suggest that spread of species to southern Africa is recent and was likely facilitated by large mammal herbivores feeding on Solanum fruits (African elephant, impala). • Rather than a linear ‘Out Of Africa’ sequence, our results are more consistent with an initial event into Asia, and subsequent wide dispersion and differentiation across Africa driven by large mammalian herbivores. Our evolutionary results will impact future work on eggplant domestication and use of wild relatives in breeding of this increasingly important solanaceous crop.Peer reviewe

Crop wild relatives of the brinjal eggplant ( Solanum melongena ): Poorly represented in genebanks and many species at risk of extinction

This is the publisher's Early View pdf without final pagination .© 2016 Botanical Society of America. This is an open access article, available to all readers online, published under a creative commons license. The attached file is the published version of the article

Emerging New Crop Pests: Ecological Modelling and Analysis of the South American Potato Psyllid Russelliana solanicola (Hemiptera: Psylloidea) and Its Wild Relatives

© 2017 Syfert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models

<div><p>Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as “feature types” in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.</p> </div

The potato pest Russelliana solanicola Tuthill (Hemiptera: Psylloidea): taxonomy and host-plant patterns

The Neotropical jumping plant-louse Russelliana solanicola Tuthill is a potato pest and a probable vector of plant pathogens. Populations morphologically similar to those found on potatoes have been collected on plants of at least ten different families, four of which have been confirmed as hosts by the presence of immatures. This suggests that R. solanicola is either a single polyphagous species or a complex of closely related, monophagous species (host races/cryptic species). Results of our analyses of multiple morphometric characters show for both sexes a grouping of the populations of R. solanicola and a clear separation of the latter from other Russelliana species. On the other hand, within R. solanicola, there is an overlap of populations from different host-plants as well as from different geographical regions. The results of the present study strongly suggest that R. solanicola is a single, polyphagous species and the known distribution indicates that it is native to the Andes. It is likely that R. solanicola has been introduced into eastern Argentina, Brazil and Uruguay. The polyphagy together with the ability to disperse and transmit plant pathogens potentially make this species an economically important pest of potato and other crop species

Box plots of AUC values. AUC values derived from MaxEnt models fitted using different functional forms (“feature types”) and two different training datasets: herbarium (a–d) and NVS (e–h).

<p>Evaluations are made using randomly withheld test data without and with correcting geographical sampling bias (a & e) and (b & f), respectively; evaluations are made using independent LUCAS data without and with correcting for sampling bias are (c & g) and (d & h), respectively. Box plots indicate variation in AUC among 40 runs (boxes encompass 25^th and 75^th percentiles, whiskers approximate 99% of the data range, points are outliers).</p

Effects of correcting for geographical sampling bias on the rates of false presences and absences, and on the predicted extent of tree ferns (as a percentage of the total land area of New Zealand).

<p>Models were fitted to two datasets (herbarium and NVS) using MaxEnt with the feature type set as “LQ”. Model predictions were based on average predictions from the 40 runs and evaluated by using the LUCAS dataset.</p>†<p>False presences occur when a model predicts a species as present whilst observed data indicate it is absent.</p>‡<p>False absences occur when a model predicts a species as absent whilst observed data indicate it is present.</p