Land-Use Decisions Have Substantial Air Quality Health Effects.

Understanding how best to use limited land without compromising food security, health, and beneficial ecosystem functions is a critical challenge of our time. Ecosystem service assessments increasingly inform land-use decisions but seldom include the effects of land use on air quality, the largest environmental health risk. Here, we estimate and value the air quality health effects of potential land-use policies and projected trends in the United States, alongside carbon sequestration and economic returns to land, until 2051. We show that air quality health effects are of first-order importance in land-use decisions, often larger in value than carbon sequestration and economic returns combined. When air quality is properly accounted for, policies that appeared beneficial are shown to be detrimental and vice versa. Land-use-driven air quality impacts are largely from agricultural emissions and biogenic forest emissions, although incentives for reduced deforestation remain beneficial overall. Without evaluating air quality, we are unable to determine whether land-use decisions make us better or worse off.

A Conceptual Framework to Integrate Biodiversity, Ecosystem Function, and Ecosystem Service Models.

Global biodiversity and ecosystem service models typically operate independently. Ecosystem service projections may therefore be overly optimistic because they do not always account for the role of biodiversity in maintaining ecological functions. We review models used in recent global model intercomparison projects and develop a novel model integration framework to more fully account for the role of biodiversity in ecosystem function, a key gap for linking biodiversity changes to ecosystem services. We propose two integration pathways. The first uses empirical data on biodiversity-ecosystem function relationships to bridge biodiversity and ecosystem function models and could currently be implemented globally for systems and taxa with sufficient data. We also propose a trait-based approach involving greater incorporation of biodiversity into ecosystem function models. Pursuing both approaches will provide greater insight into biodiversity and ecosystem services projections. Integrating biodiversity, ecosystem function, and ecosystem service modeling will enhance policy development to meet global sustainability goals.

Mapping Ecosystem Services to Human Well-being: a toolkit to support integrated landscape management for the SDGs.

The Sustainable Development Goals (SDGs) emphasize the global and multi-dimensional nature of sustainability and thus require improving our capacity to articulate and trace the impact of ecosystem change to measures of human well-being. Yet, the integrated nature of these goals is challenging to assess without similarly integrated assessment tools. We present a new modeling toolkit, "Mapping Ecosystem Services to Human well-being" (MESH), that integrates commonly used, stand-alone ecosystem services (ES) models from the InVEST suite of models to quantify and illustrate the trade-offs and synergies across five ecosystem services and up to 10 associated SDGs. Development of the software and its functionality were informed by a broad stakeholder consultation with ministries, non-governmental organizations and civil society groups in West Africa to identify common barriers to uptake and application of modeling tools in developing countries. In light of this process, key features included in MESH are (1) integration of multiple ecosystem service (ES) models into a common modeling framework supported by a curated base data set, (2) built-in scenario generation capacity to support policy analysis, (3) visualization of outcomes and trade-offs, and (4) mapping of ecosystem service change to SDG targets and goals. We illustrate the use of MESH in a case study in the Volta basin of West Africa comparing the effectiveness of three alternative conservation prioritization approaches: (1) land cover-based, (2) topographic-based, and (3) an ecosystem service-based approach to minimize the impact of agricultural expansion. We evaluate these approaches by linking changes in service supply to potential impacts on achievement of specific SDG goals and targets.

Measurement of Basal Neurotransmitter Levels Using Convolution-Based Nonfaradaic Current Removal.

Fast-scan cyclic voltammetry permits robust subsecond measurements of in vivo neurotransmitter dynamics, resulting in its established use in elucidating these species' roles in the actions of behaving animals. However, the technique's limitations, namely the need for digital background subtraction for analytical signal resolution, have restricted the information obtainable largely to that about phasic neurotransmitter release on the second-to-minute time scale. The study of basal levels of neurotransmitters and their dynamics requires a means of isolating the portion of the background current arising from neurotransmitter redox reactions. Previously, we reported on the use of a convolution-based method for prediction of the resistive-capacitive portion of the carbon-fiber microelectrode background signal, to improve the information content of background-subtracted data. Here we evaluated this approach for direct analytical signal isolation. First, protocol modifications (i.e., applied waveform and carbon-fiber type) were optimized to permit simplification of the interfering background current to components that are convolution-predictable. It was found that the use of holding potentials of at least 0.0 V, as well as the use of pitch-based carbon fibers, improved the agreement between convolution predictions and the observed background. Subsequently, it was shown that measurements of basal dopamine concentrations are possible with careful control of the electrode state. Successful use of this approach for measurement of in vivo basal dopamine levels is demonstrated, suggesting the approach may serve as a useful tool in expanding the capabilities of fast-scan cyclic voltammetry.

Removal of Differential Capacitive Interferences in Fast-Scan Cyclic Voltammetry.

Due to its high spatiotemporal resolution, fast-scan cyclic voltammetry (FSCV) at carbon-fiber microelectrodes enables the localized in vivo monitoring of subsecond fluctuations in electroactive neurotransmitter concentrations. In practice, resolution of the analytical signal relies on digital background subtraction for removal of the large current due to charging of the electrical double layer as well as surface faradaic reactions. However, fluctuations in this background current often occur with changes in the electrode state or ionic environment, leading to nonspecific contributions to the FSCV data that confound data analysis. Here, we both explore the origin of such shifts seen with local changes in cations and develop a model to account for their shape. Further, we describe a convolution-based method for removal of the differential capacitive contributions to the FSCV current. The method relies on the use of a small-amplitude pulse made prior to the FSCV sweep that probes the impedance of the system. To predict the nonfaradaic current response to the voltammetric sweep, the step current response is differentiated to provide an estimate of the system's impulse response function and is used to convolute the applied waveform. The generated prediction is then subtracted from the observed current to the voltammetric sweep, removing artifacts associated with electrode impedance changes. The technique is demonstrated to remove select contributions from capacitive characteristics changes of the electrode both in vitro (i.e., in flow-injection analysis) and in vivo (i.e., during a spreading depression event in an anesthetized rat).

Multivariate Curve Resolution for Signal Isolation from Fast-Scan Cyclic Voltammetric Data.

The use of multivariate analysis techniques, such as principal component analysis-inverse least-squares (PCA-ILS), has become standard for signal isolation from in vivo fast-scan cyclic voltammetric (FSCV) data due to its superior noise removal and interferent-detection capabilities. However, the requirement of collecting separate training data for PCA-ILS model construction increases experimental complexity and, as such, has been the source of recent controversy. Here, we explore an alternative method, multivariate curve resolution-alternating least-squares (MCR-ALS), to circumvent this issue while retaining the advantages of multivariate analysis. As compared to PCA-ILS, which relies on explicit user definition of component number and profiles, MCR-ALS relies on the unique temporal signatures of individual chemical components for analyte-profile determination. However, due to increased model freedom, proper deployment of MCR-ALS requires careful consideration of the model parameters and the imposition of constraints on possible model solutions. As such, approaches to achieve meaningful MCR-ALS models are characterized. It is shown, through use of previously reported techniques, that MCR-ALS can produce similar results to PCA-ILS and may serve as a useful supplement or replacement to PCA-ILS for signal isolation from FSCV data.

An implantable multimodal sensor for oxygen, neurotransmitters, and electrophysiology during spreading depolarization in the deep brain.

Brain tissue injury is often accompanied by spreading depolarization (SD) events, marked by widespread cellular depolarization and cessation of neuronal firing. SD recruits viable tissue into the lesion, making it a focus for intervention. During SD, drastic fluctuations occur in ion gradients, extracellular neurotransmitter concentrations, cellular metabolism, and cerebral blood flow. Measuring SD requires a multimodal approach to capture the array of changes. However, the use of multiple sensors can inflict tissue damage. Here, we use carbon-fiber microelectrodes to characterize several aspects of SD with a single, minimally invasive sensor in the deep brain region of the nucleus accumbens. Fast-scan cyclic voltammetry detects large changes in oxygen, which reflect the balance between cerebral blood flow and energy consumption, and also supraphysiological release of electroactive neurotransmitters (i.e., dopamine). We verify waves of SD with concurrent single-unit or DC potential electrophysiological recordings. The single-unit recordings reveal bursts of action potentials followed by inactivity. The DC potentials exhibit a slow negative voltage shift in the extracellular space indicative of wide-spread cellular depolarization. Here, we characterize the multiple modalities of our sensor and demonstrate its utility for improved SD recordings.

Construction of Training Sets for Valid Calibration of in Vivo Cyclic Voltammetric Data by Principal Component Analysis.

Principal component regression, a multivariate calibration technique, is an invaluable tool for the analysis of voltammetric data collected in vivo with acutely implanted microelectrodes. This method utilizes training sets to separate cyclic voltammograms into contributions from multiple electroactive species. The introduction of chronically implanted microelectrodes permits longitudinal measurements at the same electrode and brain location over multiple recordings. The reliability of these measurements depends on a consistent calibration methodology. One published approach has been the use of training sets built with data from separate electrodes and animals to evaluate neurochemical signals in multiple subjects. Alternatively, responses to unpredicted rewards have been used to generate calibration data. This study addresses these approaches using voltammetric data from three different experiments in freely moving rats obtained with acutely implanted microelectrodes. The findings demonstrate critical issues arising from the misuse of principal component regression that result in significant underestimates of concentrations and improper statistical model validation that, in turn, can lead to inaccurate data interpretation. Therefore, the calibration methodology for chronically implanted microelectrodes needs to be revisited and improved before measurements can be considered reliable.

Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation.

Yield gaps, here defined as the difference between actual and attainable yields, provide a framework for assessing opportunities to increase agricultural productivity. Previous global assessments, centred on a single year, were unable to identify temporal variation. Here we provide a spatially and temporally comprehensive analysis of yield gaps for ten major crops from 1975 to 2010. Yield gaps have widened steadily over most areas for the eight annual crops and remained static for sugar cane and oil palm. We developed a three-category typology to differentiate regions of 'steady growth' in actual and attainable yields, 'stalled floor' where yield is stagnated and 'ceiling pressure' where yield gaps are closing. Over 60% of maize area is experiencing 'steady growth', in contrast to ∼12% for rice. Rice and wheat have 84% and 56% of area, respectively, experiencing 'ceiling pressure'. We show that 'ceiling pressure' correlates with subsequent yield stagnation, signalling risks for multiple countries currently realizing gains from yield growth.

Mapping microarchitectural degeneration in the dilated ascending aorta with ex vivo diffusion tensor imaging.

Aims: Thoracic aortic aneurysms (TAAs) carry a risk of catastrophic dissection. Current strategies to evaluate this risk entail measuring aortic diameter but do not image medial degeneration, the cause of TAAs. We sought to determine if the advanced magnetic resonance imaging (MRI) acquisition strategy, diffusion tensor imaging (DTI), could delineate medial degeneration in the ascending thoracic aorta. Methods and results: Porcine ascending aortas were subjected to enzyme microinjection, which yielded local aortic medial degeneration. These lesions were detected by DTI, using a 9.4 T MRI scanner, based on tensor disorientation, disrupted diffusion tracts, and altered DTI metrics. High-resolution spatial analysis revealed that fractional anisotropy positively correlated, and mean and radial diffusivity inversely correlated, with smooth muscle cell (SMC) and elastin content (P < 0.001 for all). Ten operatively harvested human ascending aorta samples (mean subject age 61.6 ± 13.3 years, diameter range 29-64 mm) showed medial pathology that was more diffuse and more complex. Nonetheless, DTI metrics within an aorta spatially correlated with SMC, elastin, and, especially, glycosaminoglycan (GAG) content. Moreover, there were inter-individual differences in slice-averaged DTI metrics. Glycosaminoglycan accumulation and elastin degradation were captured by reduced fractional anisotropy (R2 = 0.47, P = 0.043; R2 = 0.76, P = 0.002), with GAG accumulation also captured by increased mean diffusivity (R2 = 0.46, P = 0.045) and increased radial diffusivity (R2 = 0.60, P = 0.015). Conclusion: Ex vivo high-field DTI can detect ascending aorta medial degeneration and can differentiate TAAs in accordance with their histopathology, especially elastin and GAG changes. This non-destructive window into aortic medial microstructure raises prospects for probing the risks of TAAs beyond lumen dimensions.

Mapping the planet's critical areas for biodiversity and nature's contributions to people.

Meeting global commitments to conservation, climate, and sustainable development requires consideration of synergies and tradeoffs among targets. We evaluate the spatial congruence of ecosystems providing globally high levels of nature's contributions to people, biodiversity, and areas with high development potential across several sectors. We find that conserving approximately half of global land area through protection or sustainable management could provide 90% of the current levels of ten of nature's contributions to people and meet minimum representation targets for 26,709 terrestrial vertebrate species. This finding supports recent commitments by national governments under the Global Biodiversity Framework to conserve at least 30% of global lands and waters, and proposals to conserve half of the Earth. More than one-third of areas required for conserving nature's contributions to people and species are also highly suitable for agriculture, renewable energy, oil and gas, mining, or urban expansion. This indicates potential conflicts among conservation, climate and development goals.

Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050.

Based on an extensive model intercomparison, we assessed trends in biodiversity and ecosystem services from historical reconstructions and future scenarios of land-use and climate change. During the 20th century, biodiversity declined globally by 2 to 11%, as estimated by a range of indicators. Provisioning ecosystem services increased several fold, and regulating services decreased moderately. Going forward, policies toward sustainability have the potential to slow biodiversity loss resulting from land-use change and the demand for provisioning services while reducing or reversing declines in regulating services. However, negative impacts on biodiversity due to climate change appear poised to increase, particularly in the higher-emissions scenarios. Our assessment identifies remaining modeling uncertainties but also robustly shows that renewed policy efforts are needed to meet the goals of the Convention on Biological Diversity.

Projecting global oil palm expansion under zero-deforestation commitments: Direct and indirect land use change impacts.

In the last three decades, global production of oil palm has boomed, which has partly come at the expense of tropical rainforests. Recognizing this, many companies operating in the palm oil industry have committed to eliminate deforestation from their operations, often referred to as zero-deforestation commitments (ZDCs). Here, we estimate that if ZDCs are fully adopted and enforced across all sectors and geographies, the global extent of oil palm plantations may be 11 M ha or 40% smaller in 2030 than in a business-as-usual (BAU) scenario that assumes no compliance with ZDCs. As a result of such land-sparing effects, we estimate that 96 M ha of forests are saved from conversion, of which, 17% would otherwise have been converted (directly or indirectly) due to expanding oil palm plantations. Overall, these figures suggest that ZDCs have the potential to deliver major environmental benefits if they are fully adopted and enforced.

Mapping the planet's critical natural assets.

Sustaining the organisms, ecosystems and processes that underpin human wellbeing is necessary to achieve sustainable development. Here we define critical natural assets as the natural and semi-natural ecosystems that provide 90% of the total current magnitude of 14 types of nature's contributions to people (NCP), and we map the global locations of these critical natural assets at 2 km resolution. Critical natural assets for maintaining local-scale NCP (12 of the 14 NCP) account for 30% of total global land area and 24% of national territorial waters, while 44% of land area is required to also maintain two global-scale NCP (carbon storage and moisture recycling). These areas overlap substantially with cultural diversity (areas containing 96% of global languages) and biodiversity (covering area requirements for 73% of birds and 66% of mammals). At least 87% of the world's population live in the areas benefitting from critical natural assets for local-scale NCP, while only 16% live on the lands containing these assets. Many of the NCP mapped here are left out of international agreements focused on conserving species or mitigating climate change, yet this analysis shows that explicitly prioritizing critical natural assets and the NCP they provide could simultaneously advance development, climate and conservation goals.

Spectrophotometric titration of bimetallic metal cation binding in polyamido(amine) dendrimer templates.

Spectrophotometric titration and a binding isotherm were used to accurately assess the loading capacity of generation four polyamido(amine) (PAMAM) dendrimer templates with terminal alcohol groups (G4-OH). Preparation of bimetallic G4-OH dendrimer-encapsulated metal nanoclusters (DENs) necessitates knowledge of the precise metal-ion binding capacity. The binding of metal ions such as Pt(2+) and Pd(2+) has proven difficult to assess via UV-vis spectroscopy because the absorbance shifts associated with metal-ion binding within the dendrimer template are masked by the absorbance of the PAMAM dendrimer itself. In contrast, the binding of Cu(2+) to G4-OH PAMAM dendrimer results in a strong, distinct absorption band at 300 nm, making UV-vis spectrophotometric titration with copper straightforward. Here we use copper binding as a means to assess the number of binding sites remaining within the PAMAM G4-OH dendrimer after the complexation of a specified molar excess of Pd(2+) or Pt(2+). In addition, we use a binding isotherm to mathematically estimate the loading capacity of the dendrimer in each case. The loading capacities for M(2+) in the G4-OH dendrimer were found to be ∼16 for copper alone, ∼21 for copper combined with palladium, and ∼25 for copper combined with platinum.

Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors.

Mapping suitable land for development is essential to land use planning efforts that aim to model, anticipate, and manage trade-offs between economic development and the environment. Previous land suitability assessments have generally focused on a few development sectors or lack consistent methodologies, thereby limiting our ability to plan for cumulative development pressures across geographic regions. Here, we generated 1-km spatially-explicit global land suitability maps, referred to as "development potential indices" (DPIs), for 13 sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). To do so, we applied spatial multi-criteria decision analysis techniques that accounted for both resource potential and development feasibility. For each DPI, we examined both uncertainty and sensitivity, and spatially validated the map using locations of planned development. We illustrate how these DPIs can be used to elucidate potential individual sector expansion and cumulative development patterns.

Global modeling of nature's contributions to people.

The magnitude and pace of global change demand rapid assessment of nature and its contributions to people. We present a fine-scale global modeling of current status and future scenarios for several contributions: water quality regulation, coastal risk reduction, and crop pollination. We find that where people's needs for nature are now greatest, nature's ability to meet those needs is declining. Up to 5 billion people face higher water pollution and insufficient pollination for nutrition under future scenarios of land use and climate change, particularly in Africa and South Asia. Hundreds of millions of people face heightened coastal risk across Africa, Eurasia, and the Americas. Continued loss of nature poses severe threats, yet these can be reduced 3- to 10-fold under a sustainable development scenario.

Hitchhiker's Guide to Voltammetry: Acute and Chronic Electrodes for in Vivo Fast-Scan Cyclic Voltammetry.

Fast-scan cyclic voltammetry (FSCV) has been used for over 20 years to study rapid neurotransmission in awake and behaving animals. These experiments were first carried out with carbon-fiber microelectrodes (CFMs) encased in borosilicate glass, which can be inserted into the brain through micromanipulators and guide cannulas. More recently, chronically implantable CFMs constructed with small diameter fused-silica have been introduced. These electrodes can be affixed in the brain with minimal tissue response, which permits longitudinal measurements of neurotransmission in single recording locations during behavior. Both electrode designs have been used to make novel discoveries in the fields of neurobiology, behavioral neuroscience, and psychopharmacology. The purpose of this Review is to address important considerations for the use of FSCV to study neurotransmitters in awake and behaving animals, with a focus on measurements of striatal dopamine. Common issues concerning experimental design, data collection, and calibration are addressed. When necessary, differences between the two methodologies (acute vs chronic recordings) are discussed. The topics raised in this Review are particularly important as the field moves beyond dopamine toward new neurochemicals and brain regions.

Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data.

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.