Synergies and tradeoffs among Sustainable Development Goals across boundaries in a metacoupled world.

Synergies and tradeoffs among the United Nations Sustainable Development Goals (SDGs) within specific locations have been widely studied. However, there is little understanding of SDG synergies and tradeoffs across spatial/administrative boundaries although the world is increasingly interconnected and the United Nations aims to achieve SDGs everywhere by 2030. To fill such an important gap, we introduce a new theoretical framework and develop a general procedure of applying the framework to empirically evaluate SDG synergies and tradeoffs within and across boundaries, based on the concept of metacoupling. We work through our framework using the examples of tourism and panda loans between the globally important Wolong Nature Reserve for panda conservation and the rest of the world to evaluate their effects on six SDGs in Wolong and the other 66 panda reserves. Our analyses uncover a total of 17 synergies and two tradeoffs, of which 10 synergies and one tradeoff are internal to Wolong, while seven synergies and one tradeoff occur across reserve boundaries. Given the first empirical evidence about cross-boundary synergies and tradeoffs, it is our hope that this study provides a foundation for further research to reveal more SDG synergies and tradeoffs across boundaries worldwide. The findings will be essential to enhance SDG synergies and reduce tradeoffs across boundaries for achieving SDGs everywhere.

Assessing progress towards sustainable development over space and time.

To address global challenges1-4, 193 countries have committed to the 17 United Nations Sustainable Development Goals (SDGs)5. Quantifying progress towards achieving the SDGs is essential to track global efforts towards sustainable development and guide policy development and implementation. However, systematic methods for assessing spatio-temporal progress towards achieving the SDGs are lacking. Here we develop and test systematic methods to quantify progress towards the 17 SDGs at national and subnational levels in China. Our analyses indicate that China's SDG Index score (an aggregate score representing the overall performance towards achieving all 17 SDGs) increased at the national level from 2000 to 2015. Every province also increased its SDG Index score over this period. There were large spatio-temporal variations across regions. For example, eastern China had a higher SDG Index score than western China in the 2000s, and southern China had a higher SDG Index score than northern China in 2015. At the national level, the scores of 13 of the 17 SDGs improved over time, but the scores of four SDGs declined. This study suggests the need to track the spatio-temporal dynamics of progress towards SDGs at the global level and in other nations.

Interactive spatial scale effects on species distribution modeling: The case of the giant panda.

Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km2), we found that the models built at the next-largest extent (500 km2) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species' habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.

Uncertainty of future projections of species distributions in mountainous regions.

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Impact of choice of future climate change projection on growth chamber experimental outcomes: a preliminary study in potato.

Understanding the impacts of climate change on agriculture is essential to ensure adequate future food production. Controlled growth experiments provide an effective tool for assessing the complex effects of climate change. However, a review of the use of climate projections in 57 previously published controlled growth studies found that none considered within-season variations in projected future temperature change, and few considered regional differences in future warming. A fixed, often arbitrary, temperature perturbation typically was applied for the entire growing season. This study investigates the utility of employing more complex climate change scenarios in growth chamber experiments. A case study in potato was performed using three dynamically downscaled climate change projections for the mid-twenty-first century that differ in terms of the timing during the growing season of the largest projected temperature changes. The climate projections were used in growth chamber experiments for four elite potato cultivars commonly planted in Michigan's major potato growing region. The choice of climate projection had a significant influence on the sign and magnitude of the projected changes in aboveground biomass and total tuber count, whereas all projections suggested an increase in total tuber weight and a decrease in specific gravity, a key market quality trait for potato, by mid-century. These results demonstrate that the use of more complex climate projections that extend beyond a simple incremental change can provide additional insights into the future impacts of climate change on crop production and the accompanying uncertainty.

Possible connections of the opposite trends in Arctic and Antarctic sea-ice cover.

Sea ice is an important component of the global climate system and a key indicator of climate change. A decreasing trend in Arctic sea-ice concentration is evident in recent years, whereas Antarctic sea-ice concentration exhibits a generally increasing trend. Various studies have investigated the underlying causes of the observed trends for each region, but possible linkages between the regional trends have not been studied. Here, we hypothesize that the opposite trends in Arctic and Antarctic sea-ice concentration may be linked, at least partially, through interdecadal variability of the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). Although evaluation of this hypothesis is constrained by the limitations of the sea-ice cover record, preliminary statistical analyses of one short-term and two long-term time series of observed and reanalysis sea-ice concentrations data suggest the possibility of the hypothesized linkages. For all three data sets, the leading mode of variability of global sea-ice concentration is positively correlated with the AMO and negatively correlated with the PDO. Two wave trains related to the PDO and the AMO appear to produce anomalous surface-air temperature and low-level wind fields in the two polar regions that contribute to the opposite changes in sea-ice concentration.

Changing human landscapes under a changing climate: considerations for climate assessments.

Climate change is a fundamental aspect of the Anthropocene. Climate assessments are frequently undertaken to evaluate climate change impacts, vulnerability, and adaptive capacity. Assessments are complex endeavors with numerous challenges. Five aspects of a climate assessment that can be particularly challenging are highlighted: choice of assessment strategy, incorporation of spatial linkages and interactions, the constraints of climate observations, interpretation of a climate projection ensemble, uncertainty associated with weather/climate dependency models, and consideration of landscape-climate influences. In addition, a climate assessment strategy that incorporates both traditional "top-down" and "bottom-up" methods is proposed for assessments of adaptation options at the local/regional scale. Uncertainties associated with climate observations and projections and with weather/climate dependency (i.e., response) models are incorporated into the assessment through the "top-down" component, and stakeholder knowledge and experience are included through the "bottom-up" component. Considerable further research is required to improve assessment strategies and the usefulness and usability of assessment findings. In particular, new methods are needed which better incorporate spatial linkages and interactions, yet maintain the fine grain detail needed for decision making at the local and regional scales. Also, new methods are needed which go beyond sensitivity analyses of the relative contribution of land use and land cover changes on local/regional climate to more explicitly consider landscape-climate interactions in the context of uncertain future climates. Assessment teams must clearly communicate the choices made when designing an assessment and recognize the implications of these choices on the interpretation and application of the assessment findings.