Implications of different population model structures for management of threatened plants

Population viability analysis (PVA) is a reliable tool for ranking management options for a range of species despite parameter uncertainty. No one has yet investigated whether this holds true for model uncertainty for species with complex life histories and for responses to multiple threats. We tested whether a range of model structures yielded similar rankings of management and threat scenarios for 2 plant species with complex postfire responses. We examined 2 contrasting species from different plant functional types: an obligate seeding shrub and a facultative resprouting shrub. We exposed each to altered fire regimes and an additional, species‐specific threat. Long‐term demographic data sets were used to construct an individual‐based model (IBM), a complex stage‐based model, and a simple matrix model that subsumes all life stages into 2 or 3 stages. Agreement across models was good under some scenarios and poor under others. Results from the simple and complex matrix models were more similar to each other than to the IBM. Results were robust across models when dominant threats are considered but were less so for smaller effects. Robustness also broke down as the scenarios deviated from baseline conditions, likely the result of a number of factors related to the complexity of the species’ life history and how it was represented in a model. Although PVA can be an invaluable tool for integrating data and understanding species’ responses to threats and management strategies, this is best achieved in the context of decision support for adaptive management alongside multiple lines of evidence and expert critique of model construction and output.     

A review of public acceptance of nature-based solutions: The ‘why’, ‘when’, and ‘how’ of success for disaster risk reduction measures

Nature-based solutions (NbS) are increasingly recognized as sustainable approaches to address societal challenges. Disaster risk reduction (DRR) has benefited by moving away from purely ‘grey’ infrastructure measures towards NbS. However, this shift also furthers an increasing trend of reliance on public acceptance to plan, implement and manage DRR measures. In this review, we examine how unique NbS characteristics relate to public acceptance through a comparison with grey measures, and we identify influential acceptance factors related to individuals, society, and DRR measures. Based on the review, we introduce the PA-NbS model that highlights the role of risk perception, trust, competing societal interests, and ecosystem services. Efforts to increase acceptance should focus on providing and promoting awareness of benefits combined with effective communication and collaboration. Further research is required to understand interconnections among identified factors and how they can be leveraged for the success and further uptake of NbS.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13280-021-01502-4.     

Assessing four methods for establishing native plants on urban vacant land

Urban greening increases vegetation and can restore ecological functions to urban systems. It has ties to restoration ecology, which aims to return degraded land to diverse, functional ecosystems. Both practices can be applied to maximizing ecosystem services and habitat in vacant lots, which are abundant in post-industrial cities, including Chicago, Illinois (USA), where our study took place. We tested four methods for increasing native plant diversity in vacant lots, ranging from low input to resource-intensive: seed bombing, broadcast seeding, planting plugs, and gardening. After three growing seasons, we assessed the growth of eight target native species and all non-target species. We expected that intensive treatments would have more target species stems and flowers and fewer non-target species, but we found that less-intensive options often produce equal or better results. From this, we recommend broadcast seeding as a viable, low-cost method for improving habitat and biodiversity in vacant lots.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01383-z) contains supplementary material, which is available to authorized users.     

Incorporating stakeholder decision support needs into an integrated regional Earth system model

A new modeling effort exploring the opportunities, constraints, and interactions between mitigation and adaptation at regional scale is utilizing stakeholder engagement in an innovative approach to guide model development and demonstration, including uncertainty characterization, to effectively inform regional decision making. This project, the integrated Regional Earth System Model (iRESM), employs structured stakeholder interactions and literature reviews to identify the most relevant adaptation and mitigation alternatives and decision criteria for each regional application of the framework. The information is used to identify important model capabilities and to provide a focus for numerical experiments. This paper presents the stakeholder research results from the first iRESM pilot region. The pilot region includes the Great Lakes Basin in the Midwest portion of the United States as well as other contiguous states. This geographic area (14 states in total) permits cohesive modeling of hydrologic systems while also providing strong gradients in climate, demography, land cover/land use, and energy supply and demand. The results from the stakeholder research indicate that, for this region, iRESM should prioritize addressing adaptation alternatives in the water resources, urban infrastructure, and agriculture sectors, including water conservation, expanded water quality monitoring, altered reservoir releases, lowered water intakes, urban infrastructure upgrades, increased electric power reserves in urban areas, and land use management/crop selection changes. For mitigation in this region, the stakeholder research implies that iRESM should focus on policies affecting the penetration of renewable energy technologies, and the costs and effectiveness of energy efficiency, bioenergy production, wind energy, and carbon capture and sequestration.     

Pastoral farmland habitat diversity in south-east Ireland

In this study, habitat surveys were undertaken on 50 grass-based farms in SE Ireland and data digitised onto aerial photography. Additional data i.e. stocking rates, and participation (or otherwise) in the Irish Rural Environment Protection Scheme (REPS) were collected and analysed as possible explanatory variables for farm habitat composition.Results indicated that approximately 14.3% of the land area of sampled farms comprised of semi-natural habitat types, a proportion substantially greater than has been reported for many other European countries. The most frequently recorded semi-natural habitats included, field boundaries, scrub, and deciduous and riparian woodlands.Multivariate analysis of farm habitat configuration showed a strong dichotomy between dairy and non-dairy farming systems. Habitats such as intensively managed grassland and built ground were closely associated with dairy-based enterprises. In contrast, the incidence of other habitat types was associated with non-dairy and/or REPS participating enterprises. The ecological quality of field boundaries as assessed by the Field Boundary Evaluation and Grading System (FBEGS) Index was significantly greater on dairy, compared with dry-stock farms.This dichotomy in farm habitat composition is not reflected within current Agri-Environment (AE) policy. Integration of locally important drivers of habitat diversity into the design and implementation of AE policy, is integral to the successful performance of AE schemes.     

Effect of two major N-nitroso hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) metabolites on earthworm reproductive success

Soil and topical tests were employed to investigate the effect of two N-nitroso metabolites of RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine) on earthworm reproduction. The lowest observed effect concentration (LOEC) for cocoon production and hatching was 50mg/kg for both hexahydro-1-nitroso-3,5-dinitro-1,3,5-triazine (MNX) and hexahydro-1,3,5-trinitroso-1,3,5-triazine (TNX) in soil. MNX and TNX also significantly affected cocoon hatching in soil (p<0.001) and in topical tests (p=0.001). The LOECs for cocoon hatching were 1 and 10mg/kg for MNX and TNX in soil, respectively, and 10mg/L in the topical test. Greater than 100mg/kg MNX and TNX completely inhibited cocoon hatching. In soil, the EC20 values for MNX were 8.7 and 8.8mg/kg for cocoon and juvenile production, respectively, compared to 9.2 and 9.1mg/kg for TNX, respectively. The EC20 values for the total number of cocoon hatchlings were 3.1 and 4.7mg/kg for MNX and TNX, respectively, in soil and 4.5 and 3.1mg/L in the topical test. Both MNX and TNX inhibited cocoon production and hatching, suggesting that they may have a negative affect on soil ecosystems at contaminated sites.     

Refinery evaluation of optical imaging to locate fugitive emissions

Fugitive emissions account for approximately 50% of total hydrocarbon emissions from process plants. Federal and state regulations aiming at controlling these emissions require refineries and petrochemical plants in the United States to implement a Leak Detection and Repair Program (LDAR). The current regulatory work practice, U.S. Environment Protection Agency Method 21, requires designated components to be monitored individually at regular intervals. The annual costs of these LDAR programs in a typical refinery can exceed US$1,000,000. Previous studies have shown that a majority of controllable fugitive emissions come from a very small fraction of components. The Smart LDAR program aims to find cost-effective methods to monitor and reduce emissions from these large leakers. Optical gas imaging has been identified as one such technology that can help achieve this objective. This paper discusses a refinery evaluation of an instrument based on backscatter absorption gas imaging technology. This portable camera allows an operator to scan components more quickly and image gas leaks in real time. During the evaluation, the instrument was able to identify leaking components that were the source of 97% of the total mass emissions from leaks detected. More than 27,000 components were monitored. This was achieved in far less time than it would have taken using Method 21. In addition, the instrument was able to find leaks from components that are not required to be monitored by the current LDAR regulations. The technology principles and the parameters that affect instrument performance are also discussed in the paper.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.