Social context influences cue-mediated recruitment in an invasive social wasp

Social insects often serve as model systems for communication and recruitment studies, and yet, it remains controversial whether social vespid wasps can reliably communicate resource information to nestmates. In this study, I present empirical evidence that foraging strategies depend on the initial assessment of resource size and potential competition by foraging yellowjackets. The context dependent foraging behavior of Vespula pensylvanica provides a potential explanation for the inconsistent reports of the existence of recruitment communication in vespid wasps. Furthermore, life history traits may influence yellowjacket foraging behavior; annual V. pensylvanica colonies, whose foragers routinely patrol near the nest, exhibited increased bait visitation in response to the return of successful foragers, whereas perennial colonies did not. These behavioral disparities provide insight into how foraging strategies and search patterns may shift with colony size and longevity. In experiments that investigate the effects of visual cues of conspecifics and bait dispersion, foraging decisions corresponded with expectations of yellowjackets integrating resource quantity and access into a perception of demand. When resource competition could be assessed as high, V. pensylvanica foragers quickly exploited the bait closest to their colony regardless of occupation by other wasps; however, foragers preferred visiting unoccupied baits in situations where competition could be perceived as low. Moreover, a meta-analysis revealed that context-dependent, cue-mediated recruitment was widespread in Vespidae, where such foraging behaviors changed with habitat and the potential for resource competition. Such plastic foraging strategies may contribute to the invasion success of some vespid wasps.     

The value, limitations, and challenges of employing local experts in conservation research

Abstract: Evidence suggests that the involvement of local people in conservation work increases a project's chances of success. Involving citizen scientists in research, however, raises questions about data quality. As a tool to better assess potential participants for conservation projects, we developed a knowledge gradient, K, along which community members occupy different positions on the basis of their experience with and knowledge of a research subject. This gradient can be used to refine the citizen–science concept and allow researchers to differentiate between community members with expert knowledge and those with little knowledge. We propose that work would benefit from the inclusion of select local experts because it would allow researchers to harness the benefits of local involvement while maintaining or improving data quality. We used a case study from the DeHoop Nature Preserve, South Africa, in which we conducted multiple interviews, identified and employed a local expert animal tracker, evaluated the expert's knowledge, and analyzed the data collected by the expert. The expert animal tracker J.J. created his own sampling design and gathered data on mammals. He patrolled 4653 km in 214 days and recorded 4684 mammals. He worked from a central location, and his patrols formed overlapping loops; however, his data proved neither spatially nor temporally autocorrelated. The distinctive data collected by J.J. are consistent with the notion that involving local experts can produce reliable data. We developed a conceptual model to help identify the appropriate participants for a given project on the basis of research budget, knowledge or skills needed, technical literacy requirements, and scope of the project.     

A decision framework for estimating the cost of marine plastic pollution interventions

Marine plastic pollution has emerged as one of the most pressing environmental challenges of our time. Although there has been a surge in global investment for implementing interventions to mitigate plastic pollution, there has been little attention given to the cost of these interventions. We developed a decision support framework to identify the economic, social, and ecological costs and benefits of plastic pollution interventions for different sectors and stakeholders. We calculated net cost as a function of six cost and benefit categories with the following equation: cost of implementing an intervention (direct, indirect, and nonmonetary costs) minus recovered costs and benefits (monetary and nonmonetary) produced by the interventions. We applied our framework to two quantitative case studies (a solid waste management plan and a trash interceptor) and four comparative case studies, evaluating the costs of beach cleanups and waste-to-energy plants in various contexts, to identify factors that influence the costs of plastic pollution interventions. The socioeconomic context of implementation, the spatial scale of implementation, and the time scale of evaluation all influence costs and the distribution of costs across stakeholders. Our framework provides an approach to estimate and compare the costs of a range of interventions across sociopolitical and economic contexts.     

Analysis of per- and polyfluoroalkyl substances in Houston Ship Channel and Galveston Bay following a large-scale industrial fire using ion-mobility-spectrometry-mass spectrometry

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants of concern because of their ubiquitous presence in surface and ground water; analytical methods that can be used for rapid comprehensive exposure assessment and fingerprinting of PFAS are needed. Following the fires at the Intercontinental Terminals Company (ITC) in Deer Park, TX in 2019, large quantities of PFAS-containing firefighting foams were deployed. The release of these substances into the Houston Ship Channel/Galveston Bay (HSC/GB) prompted concerns over the extent and level of PFAS contamination. A targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based study of temporal and spatial patterns of PFAS associated with this incident revealed presence of 7 species; their levels gradually decreased over a 6-month period. Because the targeted LC-MS/MS analysis was focused on about 30 PFAS molecules, it may have missed other PFAS compounds present in firefighting foams. Therefore, we utilized untargeted LC-ion mobility spectrometry-mass spectrometry (LC-IMS-MS)-based analytical approach for a more comprehensive characterization of PFAS in these water samples. We analyzed 31 samples from 9 sites in the HSC/GB that were collected over 5 months after the incident. Our data showed that additional 19 PFAS were detected in surface water of HSC/GB, most of them decreased gradually after the incident. PFAS features detected by LC-MS/MS correlated well in abundance with LC-IMS-MS data; however, LC-IMS-MS identified a number of additional PFAS, many known to be components of firefighting foams. These findings therefore illustrate that untargeted LC-IMS-MS improved our understanding of PFAS presence in complex environmental samples.     

Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction

Understanding the best way to allocate limited resources is a constant challenge for water quality improvement efforts. The synoptic approach is a tool for geographic prioritization of these efforts. It uses a benefit-cost framework to calculate indices for functional criteria in subunits (watersheds, counties) of a region and then rank the subunits. The synoptic approach was specifically designed to incorporate best professional judgment in cases where information and resources are limited. To date, the synoptic approach has been applied primarily to local or regional wetland restoration prioritization projects. The goal of this work was to develop a synoptic model for prioritizing watersheds within which suites of agricultural best management practices (BMPs) can be implemented to reduce sediment load at the watershed outlets. The model ranks candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most sediment load reduction per conservation dollar invested. The model can be applied anywhere and at many scales provided that the selected suite of BMPs is appropriate for the evaluation area’s biophysical and climatic conditions. The model was specifically developed as a tool for prioritizing BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS conservation effects assessment project (CEAP). This paper presents the testing of the model in the little river experimental watershed (LREW) which is located near Tifton, Georgia, USA and is the CEAP watershed representing the southeastern coastal plain. The application of the model to the LREW demonstrated that the model represents the physical drivers of erosion and sediment loading well. The application also showed that the model is quite responsive to social and economic drivers and is, therefore, best applied at a scale large enough to ensure differences in social and economic drivers across the candidate watersheds. The prioritization model will be used for planning purposes. Its results are visualized as maps which enable resource managers to identify watersheds within which BMP implementation would result in the most water quality improvement per conservation dollar invested.