For over a century, scientists have known that meiotic recombination rates can vary considerably among individuals, and that environmental conditions can modify recombination rates relative to the background. A variety of external and intrinsic factors such as temperature, age, sex and starvation can elicit ‘plastic’ responses in recombination rate. The influence of recombination rate plasticity on genetic diversity of the next generation has interesting and important implications for how populations evolve. Further, many questions remain regarding the mechanisms and molecular processes that contribute to recombination rate plasticity. Here, we review 100 years of experimental work on recombination rate plasticity conducted in Drosophila melanogaster. We categorize this work into four major classes of experimental designs, which we describe via classic studies in D. melanogaster. Based on these studies, we highlight molecular mechanisms that are supported by experimental results and relate these findings to studies in other systems. We synthesize lessons learned from this model system into experimental guidelines for using recent advances in genotyping technologies, to study recombination rate plasticity in non-model organisms. Specifically, we recommend (1) using fine-scale genome-wide markers, (2) collecting time-course data, (3) including crossover distribution measurements, and (4) using mixed effects models to analyse results. To illustrate this approach, we present an application adhering to these guidelines from empirical work we conducted in Drosophila pseudoobscura.This article is part of the themed issue ‘Evolutionary causes and consequences of recombination rate variation in sexual organisms’.

Characterization of ionomes has been used to uncover the basis of nutrient utilization and environmental adaptation of plants. Here, ionomic profiles were used to understand the phenotypic response of a plant to infection by genetically diverse isolates of Xylella fastidiosa, a gram-negative, xylem-limited bacterial plant pathogen. In this study, X. fastidiosa isolates were used to infect a common model host (Nicotiana tabacum 'SR1'), and leaf and sap concentrations of eleven elements together with plant colonization and symptoms were assessed. Multivariate statistical analysis revealed that changes in the ionome were significantly correlated with symptom severity and bacterial populations in host petioles. Moreover, plant ionome modification by infection could be used to differentiate the X. fastidiosa subspecies with which the plant was infected. This report establishes host ionome modification as a phenotypic response to infection.

The Sand Hills subdivision of the Southeastern Plains ecoregion has been impacted by historical land uses over the past two centuries and, with the additive effects of contemporary land use, determining reference condition for streams in this region is a challenge. We identified reference condition based on the combined use of 3 independent selection methods. Method 1 involved use of a multivariate disturbance gradient derived from several stressors, method 2 was based on variation in channel morphology, and method 3 was based on passing 6 of 7 environmental criteria. Sites selected as reference from all 3 methods were considered primary reference, whereas those selected by 2 or 1 methods were considered secondary or tertiary reference, respectively. Sites not selected by any of the methods were considered non-reference. In addition, best professional judgment (BPJ) was used to exclude some sites from any reference class, and comparisons were made to examine the utility of BPJ. Non-metric multidimensional scaling indicated that use of BPJ may help designate non-reference sites when unidentified stressors are present. The macroinvertebrate community measures Ephemeroptera, Plecoptera, Trichoptera richness and North Carolina Biotic Index showed no differences between primary and secondary reference sites when BPJ was ignored. However, there was no significant difference among primary, secondary, and tertiary reference sites when BPJ was used. We underscore the importance of classifying reference conditions, especially in regions that have endured significant anthropogenic activity. We suggest that the use of secondary reference sites may enable construction of models that target a broader set of management interests.

An understanding of how fish communities differ among river basin, watershed, and stream reach spatial scales and the factors that influence these differences can help in the design of effective conservation programs and the development of reference models that appropriately represent biota under relatively undisturbed conditions. We assessed the heterogeneity among fish assemblages in first‐ to fourth‐order stream sites from four river basins (Savannah, Chattahoochee, Cape Fear, and Pee Dee rivers) within the Sand Hills ecoregion of the southeastern USA and compared it with the heterogeneity associated with watershed and stream reach spatial scales. Fifty‐five species of fish representing 15 families were collected by electrofishing, with the most speciose families being Cyprinidae, Centrarchidae, Percidae, Ictaluridae, and Catostomidae. Constrained ordination identified clearly demarcated species assemblages among river basins as well as subbasin environmental variables that affected fish species composition; the amounts of variance attributable to basin, watershed, and stream reach spatial scales were roughly equivalent. Prominent differences occurred between Gulf of Mexico coast and Atlantic coast river basins, but differences among Atlantic coast basins were also apparent. Key variables at the watershed scale included watershed size, relief, extent of anthropogenic disturbance, and forest cover; key variables at the stream reach scale included instream habitat quality, proximity to a larger stream, and stream width. Fish assemblage collective and functional properties were more strongly influenced by variables acting at watershed and stream reach spatial scales than by differences among basins. Species richness peaked at intermediate levels of habitat quality as a likely result of biotic homogenization, indicating that the least disturbed sites within the region do not necessarily possess the highest species richness. Failure to consider this may lead to the overrating of moderately disturbed sites and the underrating of minimally disturbed sites, thus contributing to false conclusions about fish assemblage integrity. Received September 15, 2015; accepted December 15, 2015 Published online April 19, 2016

Manning's equation is used widely to predict stream discharge (Q) from hydraulic variables when logistics constrain empirical measurements of in-bank flow events. Uncertainty in Manning's roughness (n M ) is the major source of error in natural channels, and sand-bed streams pose difficulties because flow resistance is affected by flow-dependent bed configuration. Our study was designed to develop and validate models for estimating Q from channel geometry easily derived from cross-sectional surveys and available GIS data. A database was compiled consisting of 484 Q measurements from 75 sand-bed streams in Alabama, Georgia, South Carolina, North Carolina (Southeastern Plains), and Florida (Southern Coastal Plain), with six New Zealand streams included to develop statistical models to predict Q from hydraulic variables. Model error characteristics were estimated with leave-one-site-out jackknifing. Independent data of 317 Q measurements from 55 Southeastern Plains streams indicated the model (Q = A c R H 0.6906 S 0.1216 ; where A c is the channel area, R H is the hydraulic radius, and S is the bed slope) best predicted Q, based on Akaike's information criterion and root mean square error. Models also were developed from smaller Q range subsets to explore if subsets increased predictive ability, but error fit statistics suggested that these were not reasonable alternatives to the above equation. Thus, we recommend the above equation for predicting in-bank Q of unbraided, sandy streams of the Southeastern Plains.