Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo

Forest carbon stocks in rapidly developing tropical regions are highly heterogeneous, which challenges efforts to develop spatially-explicit conservation actions. In addition to field-based biodiversity information, mapping of carbon stocks can greatly accelerate the identification, protection and recovery of forests deemed to be of high conservation value (HCV). We combined airborne Light Detection and Ranging (LiDAR) with satellite imaging and other geospatial data to map forest aboveground carbon density at 30m (0.09ha) resolution throughout the Malaysian state of Sabah on the island of Borneo. We used the mapping results to assess how carbon stocks vary spatially based on forest use, deforestation, regrowth, and current forest protections. We found that unlogged, intact forests contain aboveground carbon densities averaging over 200MgCha−1, with peaks of 500MgCha−1. Critically, more than 40% of the highest carbon stock forests were discovered outside of areas designated for maximum protection. Previously logged forests have suppressed, but still high, carbon densities of 60–140MgCha−1. Our mapped distributions of forest carbon stock suggest that the state of Sabah could double its total aboveground carbon storage if previously logged forests are allowed to recover in the future. Our results guide ongoing efforts to identify HCV forests and to determine new areas for forest protection in Borneo

Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities

1. The forests of Amazonia are among the most biodiverse on Earth, yet accurately quantifying how species composition varies through space (i.e., beta‐diversity) remains a significant challenge. Here, we use high‐fidelity airborne imaging spectroscopy from the Carnegie Airborne Observatory to quantify a key component of beta‐diversity, the distance decay in species similarity through space, across three landscapes in Northern Peru. We then compared our derived distance decay relationships to theoretical expectations obtained from a Poisson Cluster Process, known to match well with empirical distance decay relationships at local scales. 2. We used an unsupervised machine learning approach to estimate spatial turnover in species composition from the imaging spectroscopy data. We first validated this approach across two landscapes using an independent dataset of forest composition in 49 forest census plots (0.1–1.5 ha). We then applied our approach to three landscapes, which together represented terra firme clay forest, seasonally flooded forest and white‐sand forest. We finally used our approach to quantify landscape‐scale distance decay relationships and compared these with theoretical distance decay relationships derived from a Poisson Cluster Process. 3. We found a significant correlation of similarity metrics between spectral data and forest plot data, suggesting that beta‐diversity within and among forest types can be accurately estimated from airborne spectroscopic data using our unsupervised approach. We also found that estimated distance decay in species similarity varied among forest types, with seasonally flooded forests showing stronger distance decay than white‐sand and terra firme forests. Finally, we demonstrated that distance decay relationships derived from the theoretical Poisson Cluster Process compare poorly with our empirical relationships. 4. Synthesis. Our results demonstrate the efficacy of using high‐fidelity imaging spectroscopy to estimate beta‐diversity and continuous distance decay in lowland tropical forests. Furthermore, our findings suggest that distance decay relationships vary substantially among forest types, which has important implications for conserving these valuable ecosystems. Finally, we demonstrate that a theoretical Poisson Cluster Process poorly predicts distance decay in species similarity as conspecific aggregation occurs across a range of nested scales within larger landscapes

Measuring Spirituality as a Universal Human Experience: A Review of Spirituality Questionnaires

Spirituality is an important theme in health research, since a spiritual orientation can help people to cope with the consequences of a serious disease. Knowledge on the role of spirituality is, however, limited, as most research is based on measures of religiosity rather than spirituality. A questionnaire that transcends specific beliefs is a prerequisite for quantifying the importance of spirituality among people who adhere to a religion or none at all. In this review, we discuss ten questionnaires that address spirituality as a universal human experience. Questionnaires are evaluated with regard to psychometric properties, item formulation and confusion with well-being and distress. Although none of the questionnaires fulfilled all the criteria, the multidimensional Spiritual Well-Being Questionnaire is promising

Integrating airborne remote sensing and field campaigns for ecology and Earth system science

In recent years, the availability of airborne imaging spectroscopy (hyperspectral) data has expanded dramatically. The high spatial and spectral resolution of these data uniquely enable spatially explicit ecological studies including species mapping, assessment of drought mortality and foliar trait distributions. However, we have barely begun to unlock the potential of these data to use direct mapping of vegetation characteristics to infer subsurface properties of the critical zone. To assess their utility for Earth systems research, imaging spectroscopy data acquisitions require integration with large, coincident ground-based datasets collected by experts in ecology and environmental and Earth science. Without coordinated, well-planned field campaigns, potential knowledge leveraged from advanced airborne data collections could be lost. Despite the growing importance of this field, documented methods to couple such a wide variety of disciplines remain sparse. We coordinated the first National Ecological Observatory Network Airborne Observation Platform (AOP) survey performed outside of their core sites, which took place in the Upper East River watershed, Colorado. Extensive planning for sample tracking and organization allowed field and flight teams to update the ground-based sampling strategy daily. This enabled collection of an extensive set of physical samples to support a wide range of ecological, microbiological, biogeochemical and hydrological studies. We present a framework for integrating airborne and field campaigns to obtain high-quality data for foliar trait prediction and document an archive of coincident physical samples collected to support a systems approach to ecological research in the critical zone. This detailed methodological account provides an example of how a multi-disciplinary and multi-institutional team can coordinate to maximize knowledge gained from an airborne survey, an approach that could be extended to other studies. The coordination of imaging spectroscopy surveys with appropriately timed and extensive field surveys, along with high-quality processing of these data, presents a unique opportunity to reveal new insights into the structure and dynamics of the critical zone. To our knowledge, this level of co-aligned sampling has never been undertaken in tandem with AOP surveys and subsequent studies utilizing this archive will shed considerable light on the breadth of applications for which imaging spectroscopy data can be leveraged