Uncovering the biogeographic pattern of the widespread nematode-trapping fungi Arthrobotrys oligospora: watershed is the key

Studies of biogeographic patterns of fungi have long been behind those of plants and animals. The presence of worldwide species, the lack of systematic sampling design and adequate sampling effort, and the lack of research units are responsible for this status. This study investigates the biogeographical patterns of Arthrobotrys oligospora, the most widespread globally distributed nematode-trapping fungi (NTF), by stratified collecting and analyzing 2,250 samples from 228 sites in Yunnan Province, China. The A. oligospora was isolated, and 149 strains were subjected to ITS, TUB, TEF and RPB2 gene sequencing and multi-gene association phylogeographic analysis. The results show that at population level A. oligospora is randomly distributed throughout Yunnan Province and has no biogeographical distribution pattern. At the genetic level, the phylogenetic tree of A. oligospora diverges into five major evolutionary clades, with a low degree of gene flow between the five clades. However, the correlation between the phylogenetic diversity of A. oligospora and geographical factors was low. There was no clear pattern in the phylogenetic clades distribution of A. oligospora either without dividing the study unit or when the grid was used as the study unit. When watersheds were used as the study unit, 67.4%, 63.3%, 65.9%, 83.3%, and 66.7% of clade 1–5 strains were distributed in the Jinsha river, Red river, Peal river, Lancang river, and Nujiang-Irawaddy river watersheds, respectively. The clades distribution of A. oligospora was highly consistent with the watersheds distribution. Training predictions of the clades distributions using randomly generated polygons were also less accurate than watersheds. These results suggest that watersheds are key to discovering the biogeographic distribution patterns of A. oligospora. The A. oligospora populations are blocked by mountains in the watershed, and gene flow barriers have occurred, which may have resulted in the formation of multiple cryptic species. Watersheds are also ideal for understanding such speciation processes, explaining factors affecting biodiversity distribution and coupling studies of plant and animal and microbial diversity

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

Remote mountainous regions are among the Earth’s last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions

Reconstructing Fire History in Mountain’s Complex Environments Using Satellite Time-Series

Historical data on vegetation fires is necessary for evaluating risks and predict future vulnerability under climatic and socio-economic change. Satellite time-series are the most effective tools to map past burned areas in mountainous regions characterized by high environmental heterogeneity and lack of base data. We evaluated existing global datasets in our study region, northwest Yunnan, China, and found that they are not suitable for accurately quantify the burning activity in these complex landscapes. Small fires, frequent seasonal clouds, rugged topography, fast vegetation recovery, patchy landcover, and no training data for training and tuning classification algorithms, were identified as the major factors limiting remote sensing applications in these areas. Based on these findings, we developed an automated burned area extraction routine which attempts to overcome these specific challenges. We created a 30 years (1987-2018) fire dataset and performed an extensive accuracy assessment. Omission and commission errors were both in the order of 20%, representing a great improvement compared to existing products

Early Spread of COVID-19 in the Air-Polluted Regions of Eight Severely Affected Countries

COVID-19 escalated into a pandemic posing several humanitarian as well as scientific challenges. We here investigated the geographical character of the early spread of the infection and correlated it with several annual satellite and ground indexes of air quality in China, the United States, Italy, Iran, France, Spain, Germany, and the United Kingdom. The time of the analysis corresponded with the end of the first wave infection in China, namely June 2020. We found more viral infections in those areas afflicted by high PM 2.5 and nitrogen dioxide values. Higher mortality was also correlated with relatively poor air quality. In Italy, the correspondence between the Po Valley pollution and SARS-CoV-2 infections and induced mortality was the starkest, originating right in the most polluted European area. Spain and Germany did not present a noticeable gradient of pollution levels causing non-significant correlations. Densely populated areas were often hotspots of lower air quality levels but were not always correlated with a higher viral incidence. Air pollution has long been recognised as a high risk factor for several respiratory-related diseases and conditions, and it now appears to be a risk factor for COVID-19 as well. As such, air pollution should always be included as a factor for the study of airborne epidemics and further included in public health policies

COVID-19 Higher Mortality in Chinese Regions With Chronic Exposure to Lower Air Quality

We investigated the geographical character of the COVID-19 infection in China and correlated it with satellite- and ground-based measurements of air quality. Controlling for population density, we found more viral infections in those prefectures (U.S. county equivalent) afflicted by high Carbon Monoxide, Formaldehyde, PM 2.5, and Nitrogen Dioxide values. Higher mortality was also correlated with relatively poor air quality. When summarizing the results at a greater administrative level, we found that the 10 provinces (U.S. state equivalent) with the highest rate of mortality by COVID-19, were often the most polluted but not the most densely populated. Air pollution appears to be a risk factor for the incidence of this disease, despite the conventionally apprehended influence of human mobility on disease dynamics from the site of first appearance, Wuhan. The raw correlations reported here should be interpreted in a broader context, accounting for the growing evidence reported by several other studies. These findings warn communities and policymakers on the implications of long-term air pollution exposure as an ecological, multi-scale public health issue

Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires

An increasing number of end-users looking for ground data about fire activity in regions where accurate official datasets are not available adopt a free-of-charge global burned area (BA) and active fire (AF) products for applications at the local scale. One of the pressing requirements from the user community is an improved ability to detect small fires (less than 50 ha), whose impact on terrestrial environments is empirically known but poorly quantified, and is often excluded from global earth system models. The newest generation of BA algorithms combines the capabilities of both the BA and AF detection approaches, resulting in a general improvement of detection compared to their predecessors. Accuracy assessments of these products have been done in several ecosystems; but more complex ones, such as regions that are characterized by frequent small fires and steep terrain has never been assessed. This study contributes to the understanding of the performance of global BA and AF products with a first assessment of four selected datasets: MODIS-based MCD45A1; MCD64A1; MCD14ML; and, ESA's Fire_CCI in a mountainous region of northwest Yunnan; P.R. China. Due to the medium to coarse resolution of the tested products and the reduced sizes of fires (often smaller than 50 ha) we used a polygon intersection assessment method where the number and locations of fire events extracted from each dataset were compared against a reference dataset that was compiled using Landsat scenes. The results for the two sample years (2006 and 2009) show that the older, non-hybrid products MCD45A1 and, MCD14ML were the best performers with Sørensen index (F1 score) reaching 0.42 and 0.26 in 2006, and 0.24 and 0.24 in 2009, respectively, while producer's accuracies (PA) were 30% and 43% in 2006, and 16% and 47% in 2009, respectively. All of the four tested products obtained higher probabilities of detection when smaller fires were excluded from the assessment, with PAs for fires bigger than 50 ha being equal to 53% and 61% in 2006, 41% and 66% in 2009 for MCD45A1 and MCD14ML, respectively. Due to the technical limitations of the satellites' sensors, a relatively low performance of the four products was expected. Surprisingly, the new hybrid algorithms produced worse results than the former two. Fires smaller than 50 ha were poorly detected by the products except for the only AF product. These findings are significant for the future design of improved algorithms aiming for increased detection of small fires in a greater diversity of ecosystems

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

Remote mountainous regions are among the Earth’s last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions

Small fires, frequent clouds, rugged terrain and no training data: a methodology to reconstruct fire history in complex landscapes

An automated burned area extraction routine that attempts to overcome the particular difficulties of remote sensing applications in complex landscapes is presented and tested in the mountainous region of northwest Yunnan, China. In particular, the lack of burned samples to use for training and testing, the rugged relief, the small size of fires and the constant presence of clouds during the rainy season heavily affecting the number of usable scenes within a year are addressed. The algorithm flows through five phases: creation of standardised difference vegetation indices time series; automatic extraction of multiclass training areas using adaptive z-score thresholds; Random Forests classification; Seeded Region Growing; and spatiotemporal clustering to form polygons representing fire events. A final database spanning the period 1987–2018 was created. Accuracy assessment of location and number of fire polygons using a stratified random sampling design showed satisfactory results with reduced omission and commission errors compared with global fire products in the same region (20 and 22% respectively). Mapping accuracy of single burned areas showed higher omission (27%) but reduced commission (13%) errors. This methodology takes a step forward towards the inclusion of regions characterised by small fires that are often poorly represented in impact assessments at the global scale

Sand and Sustainability: Finding new solutions for environmental governance of global sand resources

Sand and gravels are the unrecognised foundational material of our economies. They are mined the world over, with aggregates accounting for the largest volume of solid material extracted globally (Peduzzi, 2014). We are sing yearly 50 billion tons of sand and gravels. At the same time, these materials cannot be produced from our terrestrial, riverine and marine environments in quantities needed to meet demand from a world of 10 billion people without effective policy, planning, regulation and management. Such actions remain largely unaddressed by decision makers in public or private sectors. It is time to challenge the paradigm of infinite sand resources through constructive dialogue and solution-finding. This report aims to be the starting point from which a productive global conversation on sand extraction can begin. UNEP/GRID-Geneva report on the issue of sand and sustainability. This report was circulated at the fourth United Nations Environment Assembly in February 2019 (UNEA-4). It supported the UNEA-4 resolution 19 on Mineral Resource governance. It was the first time that sand was mentioned as an environmental issue of international relevance. This report is based on an experts round table organized by UNEP/GRID-Geneva, Swiss Federal Office for the Environment and University of Geneva in October 2018 explore potential solutions to reduce the impacts related to the extraction and use of sand world-wide.</p

Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires

An increasing number of end-users looking for ground data about fire activity in regions where accurate official datasets are not available adopt a free-of-charge global burned area (BA) and active fire (AF) products for applications at the local scale. One of the pressing requirements from the user community is an improved ability to detect small fires (less than 50 ha), whose impact on terrestrial environments is empirically known but poorly quantified, and is often excluded from global earth system models. The newest generation of BA algorithms combines the capabilities of both the BA and AF detection approaches, resulting in a general improvement of detection compared to their predecessors. Accuracy assessments of these products have been done in several ecosystems; but more complex ones, such as regions that are characterized by frequent small fires and steep terrain has never been assessed. This study contributes to the understanding of the performance of global BA and AF products with a first assessment of four selected datasets: MODIS-based MCD45A1; MCD64A1; MCD14ML; and, ESA’s Fire_CCI in a mountainous region of northwest Yunnan; P.R. China. Due to the medium to coarse resolution of the tested products and the reduced sizes of fires (often smaller than 50 ha) we used a polygon intersection assessment method where the number and locations of fire events extracted from each dataset were compared against a reference dataset that was compiled using Landsat scenes. The results for the two sample years (2006 and 2009) show that the older, non-hybrid products MCD45A1 and, MCD14ML were the best performers with Sørensen index (F1 score) reaching 0.42 and 0.26 in 2006, and 0.24 and 0.24 in 2009, respectively, while producer’s accuracies (PA) were 30% and 43% in 2006, and 16% and 47% in 2009, respectively. All of the four tested products obtained higher probabilities of detection when smaller fires were excluded from the assessment, with PAs for fires bigger than 50 ha being equal to 53% and 61% in 2006, 41% and 66% in 2009 for MCD45A1 and MCD14ML, respectively. Due to the technical limitations of the satellites’ sensors, a relatively low performance of the four products was expected. Surprisingly, the new hybrid algorithms produced worse results than the former two. Fires smaller than 50 ha were poorly detected by the products except for the only AF product. These findings are significant for the future design of improved algorithms aiming for increased detection of small fires in a greater diversity of ecosystems