Search for: All records

Abstract Contaminated drinking water is an important public health consideration in New England where well water is often found to contain arsenic and other metals such as cadmium, lead, and uranium. Chronic or high level exposure to these metals have been associated with multiple acute and chronic diseases, including cancers and impaired neurological development. While individual metal levels are often regulated, adverse health effects of metal mixtures, especially at concentrations considered safe for human consumption remain unclear. Here, we utilized a multivariate analysis that examined behavioral outcomes in the zebrafish model as a function of multiple metal chemical constituents of 92 drinking well water samples, collected in Maine and New Hampshire. To collect these samples, a citizen science approach was used, that engaged local teachers, students, and scientific partners. Our analysis of 4016 metal-mixture combinations shows that changes in zebrafish behavior are highly mixture dependent, and indicate that certain combinations of metals, especially those containing arsenic, cadmium, lead, and uranium, even at levels considered safe in drinking water, are significant drivers of behavioral toxicity. Our data emphasize the need to consider low-level chemical mixture effects and provide a framework for a more in-depth analysis of drinking water samples. We also provide evidence for the efficacy of utilizing citizen science in research, as the broader impact of this work is to empower local communities to advocate for improving their own water quality. 

A primary goal of ecological restoration is to increase biodiversity in degraded ecosystems. However, the success of restoration ecology is often assessed by measuring the response of a single functional group or trophic level to restoration, without considering how restoration affects multitrophic interactions that shape biodiversity. An ecosystem-wide approach to restoration is therefore necessary to understand whether animal responses to restoration, such as changes in biodiversity, are facilitated by changes in plant communities (plant-driven effects) or disturbance and succession resulting from restoration activities (management-driven effects). Furthermore, most restoration ecology studies focus on how restoration alters taxonomic diversity, while less attention is paid to the response of functional and phylogenetic diversity in restored ecosystems. Here, we compared the strength of plant-driven and management-driven effects of restoration on four animal communities (ground beetles, dung beetles, snakes, and small mammals) in a chronosequence of restored tallgrass prairie, where sites varied in management history (prescribed fire and bison reintroduction). Our analyses indicate that management-driven effects on animal communities were six-times stronger than effects mediated through changes in plant biodiversity. Additionally, we demonstrate that restoration can simultaneously have positive and negative effects on biodiversity through different pathways, which may help reconcile variation in restoration outcomes. Furthermore, animal taxonomic and phylogenetic diversity responded differently to restoration, suggesting that restoration plans might benefit from considering multiple dimensions of animal biodiversity. We conclude that metrics of plant diversity alone may not be adequate to assess the success of restoration in reassembling functional ecosystems. 

Site‐specific conditions, climate, and management decisions all dictate the establishment and composition of desired plant communities within grassland restorations. The uncertainty, complexity, and large size of grassland restorations necessitate monitoring plant communities across spatial and temporal scales. Remote sensing with unmanned aerial vehicles (UAVs) may provide a tool to monitor restored plant communities at various scales, but many potential applications are still unknown. In a tallgrass prairie restoration located in Franklin Grove, IL, we used UAV‐based multispectral imagery to assess the ability of spectral indices to predict ecological characteristics (plant community, plant traits, soil properties) in the summer of 2017. Using 19 sites, we calculated the moments of 26 vegetation indices and four spectral bands (green, red, red edge, near infrared). Models based on each moment and a model with all moments were estimated using ridge regression with model training based on a subset of 15 sites. Each tested for significant error reduction against a null model. We predicted mean graminoid cover, mean dead aboveground biomass, mean dry mass, and mean soil K with significant reductions in cross‐validated root mean square error. Averaged coefficients determined from cross‐validation of ridge regression models were used to develop a final predictive model of the four successfully predicted ecological characteristics. Graminoid cover and soil potassium were successfully predicted in one of the sites while the other two were not successfully predicted in any site. This study provides a path toward a new level of ease and precision in monitoring community dynamics of restored grasslands. 

Abstract Phylogenetic and species‐based taxonomic descriptions of community structure may provide complementary information about the mechanisms driving community assembly across different environments. Environmental filtering may have similar effects on taxonomic and phylogenetic diversity under the assumption of niche conservatism, whereas competitive exclusion could produce contrasting patterns in these diversity metrics. In grassland restorations, these diversity patterns might then reveal potential assembly mechanisms underlying the impacts of restoration and management conditions on community structure.We compared plant community structure (alpha diversity, composition, and within‐site beta diversity) from both phylogenetic and taxonomic perspectives. Using surveys from 120 tallgrass prairie restorations in four regions of the Midwestern United States, we examined the effects of four potential drivers or environmental gradients: precipitation in the first year of restoration, seed mix richness, time since last prescribed fire, and restoration age, and included soil conditions as a covariate.First‐year precipitation influenced taxonomic community structure, but had weak effects on phylogenetic diversity and composition. Similarly, greater seed mix richness increased taxonomic diversity but did not influence phylogenetic diversity. Taxonomic, but not phylogenetic, diversity generally was lower in older restorations and those with a longer time since the last prescribed fire. These drivers consistently explained more variation in taxonomic than phylogenetic diversity and composition, perhaps in part because species turnover was largely among related species, producing weak impacts on phylogenetic community measures.An impact of precipitation on taxonomic but not phylogenetic diversity suggests that there may not be large differences in drought tolerance among clades that would cause phylogenetic patterns to arise from this environmental filter. Declining taxonomic diversity but not phylogenetic diversity is consistent with competitive exclusion as an assembly mechanism when competition is strongest between related species.Synthesis. This research shows how studying taxonomic and phylogenetic diversity of ecosystem restorations can inform plant community ecology and help natural resource managers better predict the outcomes of restoration actions and management.