Darren Ward

Abstract Ninetinae are a group of poorly known spiders that do not fit the image of ‘daddy long-legs spiders’ (Pholcidae), the family to which they belong. They are mostly short-legged, tiny and live in arid environments. The previously monotypic Andean genus Nerudia exemplifies our poor knowledge of Ninetinae: only seven adult specimens from two localities in Chile and Argentina have been reported in the literature. We found representatives of Nerudia at 24 of 52 localities visited in 2019, mostly under rocks in arid habitats, up to 4450 m a.s.l., the highest known record for Pholcidae. With now more than 400 adult specimens, we revise the genus, describing ten new species based on morphology (including SEM) and COI barcodes. We present the first karyotype data for Nerudia and for its putative sister-genus Gertschiola. These two southern South American genera share a X1X2X3Y sex chromosome system. We model the distribution of Nerudia, showing that the genus is expected to occur in the Atacama biogeographic province (no record so far) and that its environmental niche is phylogenetically conserved. This is the first comprehensive revision of any Ninetinae genus. It suggests that focused collecting may uncover a considerable diversity of these enigmatic spiders.

Global climate change will likely exacerbate crop losses from insect pests, reducing agricultural production and threatening food security. To predict where crop losses will occur, scientists have mainly used correlative models of species’ distributions, but such models are unreliable when extrapolated to future environments. To minimize extrapolation, we developed mechanistic and hybrid models that explicitly capture range‐limiting processes, and we explored how incorporating mechanisms altered the projected impacts of climate change for an agricultural pest, the South American locust (Schistocerca cancellata). Because locusts are generalist herbivores surrounded by food, their population growth may be limited by thermal effects on digestion more than food availability. To incorporate this mechanism into a distribution model, we measured thermal effects on the consumption and defecation of field‐captured locusts and used these data to model energy gain in current and future climates. We then created hybrid models by using outputs of the mechanistic model as predictor variables in correlative models, estimating the potential distribution of gregarious outbreaking locusts based on multiple predictor sets, modeling algorithms, and climate scenarios. Based on the mechanistic model, locusts can assimilate relatively high amounts of energy throughout temperate and tropical South America; however, correlative and hybrid modeling revealed that most tropical areas are unsuitable for locusts. When estimating current distributions, the top‐ranked model was always the one fit with mechanistic predictors (i.e., the hybrid model). When projected to future climates, top‐ranked hybrid models projected range expansions that were 23‐30 percentage points smaller than those projected by correlative models. Therefore, combining the correlative and mechanistic approaches bracketed the potential outcomes of climate change and enhanced confidence where model projections agreed. Because all models projected a poleward range expansion under climate change, agriculturists should consider enhanced monitoring and management of locusts near the southern margin of the range.

Climate change will cause drastic fluctuations in agricultural ecosystems, which in turn may affect global food security. We used ecological niche modeling to predict the potential distribution for four cereal aphids (i.e., Sitobion avenae, Rhopalosiphum padi, Schizaphis graminum, and Diurphis noxia…

Plant pest invasions cost billions of Euros each year in Europe. Prediction of likely places of pest introduction could greatly help focus efforts on prevention and control and thus reduce societal costs of pest invasions. Here, we test whether generic data-driven risk maps of pest introduction, val…

Mollusca is the largest marine phylum, comprising about 23% of all named marine organisms, Mollusca systematics are still in flux, and an increase in human activities has affected Molluscan reproduction and development, strongly impacting diversity and classification. Therefore, it is necessary to e…

MaxEnt is a machine-learning-based species distribution modeling tool that is widely used to evaluate the occurrence possibility of a species. The characteristics of the algorithm requires that bioclimatic variables are appropriately selected in order for the model to predict species occurrence as p…

Climate change is predicted to increase the risk of biological invasions by increasing the availability of climatically suitable regions for invasive species. Endemic species on oceanic islands are particularly sensitive to the impact of invasive species due to increased competition for shared resou…

An understanding of the origin and formation of biodiversity and distribution patterns can provide a theoretical foundation for biodiversity conservation. In this study, phylogeny and biogeography analyses based on mitochondrial genomes and niche modeling based on occurrence records were performed t…

Insects are the focus of many recent studies suggesting population declines, but even invaluable pollination service providers such as bees lack a modern distributional synthesis. Here, we combine a uniquely comprehensive checklist of bee species distributions and >5,800,000 public bee occurrence re…

The understanding of how niche-related traits change during species invasion have prompted what is now known as the niche conservatism principle. Most studies that have tested the niche conservatism principle have focused on the extent to which the species’ climatic niches remain stable in their exo…