Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics

Spatially explicit data on tidal and waves are required as part of coastal monitoring applications (e.g., radar monitoring of coastal change) for the design of interventions to mitigate the impacts of climate change. A deployment over two tidal cycles of a low-cost Global Navigation Satellite System (GNSS) buoy at Rossall (near Fleetwood), UK demonstrated the potential to record good quality sea level and wave data within the intertidal zone. During each slack water and the following ebb tide, the sea level data were of good quality and comparable with data from nearby tide gauges on the national tide gauge network. Moreover, the GNSS receiver was able to capture wave information and these compared well with data from a commercial wave buoy situated 9.5 km offshore. Discontinuities were observed in the elevation data during flood tide, coincident with high accelerations and losing satellite signal lock. These were probably due to strong tidal currents, which, combined with spilling waves, would put the mooring line under tension and allow white water to spill over the antenna resulting in the periodic loss of GNSS signals, hence degrading the vertical solutions.

Ecosystem Services Evaluation of Nature-Based Solutions with the Help of Citizen Scientists

Ecosystem services are increasingly being considered in decision-making with respect to mitigating future climate impacts. In this respect, there is a clear need to identify how nature-based solutions (NBS) can benefit specific ecosystem services, in particular within the complex spatial and temporal dynamics that characterize most river catchments. To capture these changes, ecosystem models require spatially explicit data that are often difficult to obtain for model development and validation. Citizen science allows for the participation of trained citizen volunteers in research or regulatory activities, resulting in increased data collection and increased participation of the general public in resource management. Despite the increasing experience in citizen science, these approaches have seldom been used in the modeling of provisioning ecosystem services. In the present study, we examined the temporal and spatial drivers in nutrient delivery in a major Italian river catchment and under different NBS scenarios. Information on climate, land use, soil and river conditions, as well as future climate scenarios, were used to explore future (2050) benefits of NBS on local and catchment scale nutrient loads and nutrient export. We estimate the benefits of a reduction in nitrogen and phosphorus export to the river and the receiving waters (Adriatic Sea) with respect to the costs associated with individual and combined NBS approaches related to river restoration and catchment reforestation.

Machine Learning of Spatial Data

Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spatial machine learning.

Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning

Burrowing animals are important ecosystem engineers affecting soil properties, as their burrowing activity leads to the redistribution of nutrients and soil carbon sequestration. The magnitude of these effects depends on the spatial density and depth of such burrows, but a method to derive this type of spatially explicit data is still lacking. In this study, we test the potential of using consumer-oriented UAV RGB imagery to determine the density and depth of holes created by burrowing animals at four study sites along a climate gradient in Chile, by combining UAV data with empirical field plot observations and machine learning techniques. To enhance the limited spectral information in RGB imagery, we derived spatial layers representing vegetation type and height and used landscape textures and diversity to predict hole parameters. Across-site models for hole density generally performed better than those for depth, where the best-performing model was for the invertebrate hole density (R2 = 0.62). The best models at individual study sites were obtained for hole density in the arid climate zone (R2 = 0.75 and 0.68 for invertebrates and vertebrates, respectively). Hole depth models only showed good to fair performance. Regarding predictor importance, the models heavily relied on vegetation height, texture metrics, and diversity indices.

Relating the Strength of Density Dependence and the Spatial Distribution of Individuals

Spatial patterns in ecology contain useful information about underlying mechanisms and processes. Although there are many summary statistics used to quantify these spatial patterns, there are far fewer models that directly link explicit ecological mechanisms to observed patterns easily derived from available data. We present a model of intraspecific spatial aggregation that quantitatively relates static spatial patterning to negative density dependence. Individuals are placed according to the colonization rule consistent with the Maximum Entropy Theory of Ecology (METE), and die with probability proportional to their abundance raised to a power α, a parameter indicating the degree of density dependence. This model can therefore be interpreted as a hybridization of MaxEnt and mechanism. Our model shows quantitatively and generally that increasing density dependence randomizes spatial patterning. α = 1 recovers the strongly aggregated METE distribution that is consistent with many ecosystems empirically, and as α → 2 our prediction approaches the binomial distribution consistent with random placement. For 1 < α < 2, our model predicts more aggregation than random placement but less than METE. We additionally relate our mechanistic parameter α to the statistical aggregation parameter k in the negative binomial distribution, giving it an ecological interpretation in the context of density dependence. We use our model to analyze two contrasting datasets, a 50 ha tropical forest and a 64 m2 serpentine grassland plot. For each dataset, we infer α for individual species as well as a community α parameter. We find that α is generally larger in the tightly packed forest than the sparse grassland, and the degree of density dependence increases at smaller scales. These results are consistent with current understanding in both ecosystems, and we infer this underlying density dependence using only empirical spatial patterns. Our model can easily be applied to other datasets where spatially explicit data are available.

Demand for and Economic Value of Nonmotorized Boating Access in Rivers at US National Forests

Nonmotorized boating (NMB) is a popular recreation activity in the US National Forest System. Previous studies on NMB were from an individual river or site, which limited aggregating benefit across the system or generalizing to rivers across the country. Further, whether and how site and river characteristics affect the use of rivers for NMB activities are unknown. This study combined trip data collected from visitor surveys across the system with spatially explicit data on river characteristics in a travel cost model, and in the analysis step, characterized the economic benefit of NMB access and evaluated the effect of site and river characteristics. Net economic benefit of NMB access was estimated to be in the range of $56 to $73 per trip, depending on the modeling assumptions used. When aggregated across visits over the country, the total annual economic value of NMB access in National Forest System ranged from $92 million to $120 million. Results further suggest that site and river characteristics including water velocity, ramp availability, and rapid level were significantly related to NMB demand. Results may be useful in highlighting the use and public value of NMB access in rivers and in understanding the importance of site and river characteristics.

Implications of Bioenergy Cropping for Soil: Remote Sensing Identification of Silage Maize Cultivation and Risk Assessment Concerning Soil Erosion and Compaction

Energy transition strategies in Germany have led to an expansion of energy crop cultivation in landscape, with silage maize as most valuable feedstock. The changes in the traditional cropping systems, with increasing shares of maize, raised concerns about the sustainability of agricultural feedstock production regarding threats to soil health. However, spatially explicit data about silage maize cultivation are missing; thus, implications for soil cannot be estimated in a precise way. With this study, we firstly aimed to track the fields cultivated with maize based on remote sensing data. Secondly, available soil data were target-specifically processed to determine the site-specific vulnerability of the soils for erosion and compaction. The generated, spatially-explicit data served as basis for a differentiated analysis of the development of the agricultural biogas sector, associated maize cultivation and its implications for soil health. In the study area, located in a low mountain range region in Western Germany, the number and capacity of biogas producing units increased by 25 installations and 10,163 kW from 2009 to 2016. The remote sensing-based classification approach showed that the maize cultivation area was expanded by 16% from 7305 to 8447 hectares. Thus, maize cultivation accounted for about 20% of the arable land use; however, with distinct local differences. Significant shares of about 30% of the maize cultivation was done on fields that show at least high potentials for soil erosion exceeding 25 t soil ha−1 a−1. Furthermore, about 10% of the maize cultivation was done on fields that pedogenetically show an elevated risk for soil compaction. In order to reach more sustainable cultivation systems of feedstock for anaerobic digestion, changes in cultivated crops and management strategies are urgently required, particularly against first signs of climate change. The presented approach can regionally be modified in order to develop site-adapted, sustainable bioenergy cropping systems.

Most Root-derived Carbon Inputs Do Not Contribute to the Global Bulk Soil Carbon Pool

Plant root-derived carbon (C) inputs (Iroot) are the primary source of C in mineral bulk soil. However, a fraction of Iroot may lose directly (Iloss, e.g., via rhizosphere microbial respiration, leaching and fauna feeding) without contributing to bulk soil C pool. This loss has never been quantified, particularly at global scale, inhibiting reliable estimation of soil C dynamics. Here we integrate three observational global datasets including radiocarbon content, allocation of photosynthetically assimilated C, and root biomass distribution in 2,034 soil profiles to quantify Iroot and its contribution to the bulk soil C pool. We show that global average Iroot in the 0-200 cm soil profile is 3.5 Mg ha-1 yr-1, ~80% of which (i.e., Iloss) is lost rather than entering bulk soil. If ignoring Iloss, bulk soil C turnover will be incorrectly estimated to be four times faster. This can explain why Earth system models (in which all Iroot enters bulk soil C pools) predict much faster soil C turnover than radiocarbon-constrained estimates. Iroot decreases exponentially with soil depth, and the top 20 cm soil contains >60% of total Iroot. Actual C input to bulk soil (i.e., Iroot – Iloss) shows a similar depth distribution to Iroot. We also map Iloss and its depth distribution across the globe. Our results demonstrate the global significance of direct C losses which limit the contribution of Iroot to bulk soil C storage; and provide spatially explicit data to facilitate reliable soil C predictions via separating direct C losses from total root-derived C inputs.

Pandemic Induced Changes in Economic Activity around African Protected Areas Captured through Night-Time Light Data

The importance of tourism for development is widely recognized. Travel restrictions imposed to contain the spread of COVID-19 have brought tourism to a halt. Tourism is one of the key sectors driving change in Africa and is based exclusively on natural assets, with wildlife being the main attraction. Economic activities, therefore, are clustered around conservation and protected areas. We used night-time light data as a proxy measure for economic activity to assess change due to the pandemic. Our analysis shows that overall, 75 percent of the 8427 protected areas saw a decrease in light intensity in varying degrees in all countries and across IUCN protected area categories, including in popular protected area destinations, indicating a reduction in tourism-related economic activities. As countries discuss COVID-19 recovery, the methods using spatially explicit data illustrated in this paper can assess the extent of change, inform decision-making, and prioritize recovery efforts.

The origin, supply chain, and deforestation risk of Brazil’s beef exports

Though the international trade in agricultural commodities is worth more than $1.6 trillion/year, we still have a poor understanding of the supply chains connecting places of production and consumption and the socioeconomic and environmental impacts of this trade. In this study, we provide a wall-to-wall subnational map of the origin and supply chain of Brazilian meat, offal, and live cattle exports from 2015 to 2017, a trade worth more than $5.4 billion/year. Brazil is the world’s largest beef exporter, exporting approximately one-fifth of its production, and the sector has a notable environmental footprint, linked to one-fifth of all commodity-driven deforestation across the tropics. By combining official per-shipment trade records, slaughterhouse export licenses, subnational agricultural statistics, and data on the origin of cattle per slaughterhouse, we mapped the flow of cattle from more than 2,800 municipalities where cattle were raised to 152 exporting slaughterhouses where they were slaughtered, via the 204 exporting and 3,383 importing companies handling that trade, and finally to 152 importing countries. We find stark differences in the subnational origin of the sourcing of different actors and link this supply chain mapping to spatially explicit data on cattle-associated deforestation, to estimate the “deforestation risk” (in hectares/year) of each supply chain actor over time. Our results provide an unprecedented insight into the global trade of a deforestation-risk commodity and demonstrate the potential for improved supply chain transparency based on currently available data.