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USGS2025

Analysis of a human-mediated microbioinvasion: The global spread of the benthic foraminifer Trochammina hadai Uchio, 1962

A non-indigenous species (NIS) of benthic foraminifera was first identified in a core collected in 1993 in San Francisco Bay, California, USA, and subsequently identified as Trochammina hadai Uchio, 1962. Archived samples and literature reviews were used to determine that the species, which is native to Asia, arrived in San Francisco Bay between the early 1960s and 1983. Through molecular analyses of specimens, archived samples and literature reviews from 1930–1983, and site surveys of harbors and estuaries along the western North American seaboard in 1994–2024, in total more than 2500 samples, we documented the presence of T. hadai at 73 locations in the USA and four in Canada. Trochammina hadai has also been recovered at nine sites in Sweden, two in France, three in Brazil, and two locations at one site in Australia. The rapid temporal and geographic spread of the NIS T. hadai in a non-native location is illustrated by a time series from 1930 to 2024 in San Francisco Bay. Between 1980 and 1986, the species' range expanded from low abundance (1.5 %) at a single site to cover nearly the entire South Bay with > 70 % abundance at some locations. By 1995 and continuing into 2010, the species expanded its range into the central and northern portions of San Francisco Bay, commonly with abundances of > 30 % and sometimes exceeding 70 %. This expansion may predate 1995, but a lack of samples makes it difficult to be more precise. Unfortunately, two Pb-210 and Cs-137-dated cores (BC01 and BC02) recovered from northern South Bay and Central Bay did not clarify this point, but additional cores may. Trochammina hadai is an infaunal opportunist that thrives in polluted locations. We surmise the species was introduced along the west coast of the USA in Puget Sound between 1902 and the 1920s, with cultivated oysters and oyster larvae and associated plant matter and residual sediment. This probably also happened in some areas of France, Sweden, and Brazil, where Japanese oysters were introduced in 1966, 1970, and 1975, respectively. After World War II, commercial shipping expanded dramatically and, with it, the release of ballast water and sediment in receiving ports, which introduced NIS worldwide. This primary vector of introduction occurred in large industrial harbors in several countries, sometimes followed by secondary introductions in small industrial centers and marinas by mud attached to the anchors and anchor chains of smaller boats.

Journal of Micropalaeontology↗

USGS2022

Guidelines for volcano-observatory operations during crises: Recommendations from the 2019 Volcano Observatory Best Practices meeting

In November 2019, the fourth meeting on Volcano Observatory Best Practices workshop was held in Mexico City as a series of talks, discussions, and panels. Volcanologists from around the world offered suggestions for ways to optimize volcano-observatory crisis operations. By crisis, we mean unrest that may or may not lead to eruption, the eruption itself, or its aftermath, all of which require analysis and communications by the observatory. During a crisis, the priority of the observatory should be to acquire, process, analyze, and interpret data in a timely manner. A primary goal is to communicate effectively with the authorities in charge of civil protection. Crisis operations should rely upon exhaustive planning in the years prior to any actual unrest or eruptions. Ideally, nearly everything that observatories do during a crisis should be envisioned, prepared, and practiced prior to the actual event. Pre-existing agreements and exercises with academic and government collaborators will minimize confusion about roles and responsibilities. In the situation where planning is unfinished, observatories should prioritize close ties and communications with the land and civil-defense authorities near the most threatening volcanoes. To a large extent, volcanic crises become social crises, and any volcano observatory should have a communication strategy, a lead communicator, regular status updates, and a network of colleagues outside the observatory who can provide similar messaging to a public that desires consistent and authoritative information. Checklists permit tired observatory staff to fulfill their duties without forgetting key communications, data streams, or protocols that need regular fulfilment (Bretton et al. 2018; Newhall et al. 2020). Observatory leaders need to manage staff workload to prevent exhaustion and ensure that expertise is available as needed. Event trees and regular group discussions encourage multi-disciplinary thinking, consideration of disparate viewpoints, and documentation of all group decisions and consensus. Though regulations, roles and responsibilities differ around the world, scientists can justify their actions in the wake of an eruption if they document their work, are thoughtful and conscientious in their deliberations, and carry out protocols and procedures developed prior to volcanic unrest. This paper also contains six case studies of volcanic eruptions or observatory actions that illustrate some of the topics discussed herein. Specifically, we discuss Ambae (Vanuatu) in 2017–2018, Kīlauea (USA) in 2018, Etna (Italy) in 2018, Bárðarbunga (Iceland) in 2014, Cotopaxi (Ecuador) in 2015, and global data sharing to prepare for eruptions at Nyiragongo (Democratic Republic of Congo).

Hawaii↗

USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗

USGS2014

The Lepanto Cu–Au deposit, Philippines: A fossil hyperacidic volcanic lake complex

Hyperacidic lakes and associated solfatara in active volcanoes are the expression of magmatic gas expansion from source to surface. Here we show for the first time, that the vein system that comprises the ~ 2 Ma high-sulfidation, Lepanto copper–gold deposit in the Mankayan district (Philippines) was associated with a contemporary hyperacidic volcanic lake complex—possibly the first such lake recognized in the geological record. A 15–20‰ difference in sulfur isotopic composition between barite and sulfides and sulfosalts in the vent fumarole encrustations supports the interpretation that SO 2 -rich volcanic gas vented into the base of the lake and marginal to it and ties the mineralization directly to magmatic gas expansion, fracture propagation, and mineralization that occurred through a series of decompression steps within the feeder fracture network. These data confirm that crater lake environments such as Kawah Ijen (Java, Indonesia) provide modern day analogs of the Lepanto and other high sulfidation Cu–Au depositing environments. We also provide extensive analysis of sulfosalt–sulfide reactions during vein formation within the hyperacidic lake complex. Pyrite ± silica deposited first at high temperature followed by enargite that preserves the vapor–solid diffusion of, for example, antimony, tin, and tellurium into the vapor from the crystallizing solid. Subsolidus, intra-crystalline diffusion continued as temperature declined. Pyrite and enargite are replaced by Fe-tennantite in the lodes which initially has low Sb/(Sb + As) atomic ratios around 13.5% close to the ideal tennantite formula, but evolves to higher ratios as crystallization proceeds. Fumarole encrustation clasts and sulfosalts in the lake sediment are more highly evolved with a larger range of trace element substitutions, including antimony. Substitution of especially Zn, Te, Ag, and Sn into tennantite records metal and semi-metal fractionation between the expanding magmatic gas and deposited sulfide sublimates provides a rare insight into the fate of metals and semi-metals in the shallower parts of fracture arrays that feed modern hyperacidic lakes. These data support a growing understanding of the formation of high-sulfidation gold deposits as the consequence of single-phase expansion of gas from magmatic-gas reservoirs beneath the surface of active volcanoes without the intervention of a later aqueous fluid including groundwater. Aggressive sulfide–sulfosalt reactions, including pitting and the almost complete dissolution of earlier minerals, are persistent characteristics of the vein assemblages and precious metals typically occur late in pits or along brittle fractures. These characteristics support a hypothesis of mineral deposition at temperatures of the order of 600 °C in contrast to available fluid inclusion data from enargite that record temperatures following phase transitions in the sulfosalt during the retrograde devolution of the deposit in the presence of groundwater.

Benguet↗

USGS2009

Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997-1999, (b) Syste me pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961-2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992-1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space-time spiral curve (ST-SC) plots, (b) brightness-greenness-wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) 'zoom-in views' in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but 'equipped for irrigation' at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year-round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79-91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org). ?? 2009 Taylor & Francis.

International Journal of Remote Sensing↗

USGS2023

Climate change and the global redistribution of biodiversity: Substantial variation in empirical support for expected range shifts

Background Among the most widely predicted climate change-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution to track their climate niches. A series of commonly articulated hypotheses have emerged in the scientific literature suggesting species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to rising temperatures associated with climate change. Yet, many species are not demonstrating range shifts consistent with these expectations. Here, we evaluate the impact of anthropogenic climate change (specifically, changes in temperature and precipitation) on species’ ranges, and assess whether expected range shifts are supported by the body of empirical evidence. Methods We conducted a Systematic Review, searching online databases and search engines in English. Studies were screened in a two-stage process (title/abstract review, followed by full-text review) to evaluate whether they met a list of eligibility criteria. Data coding, extraction, and study validity assessment was completed by a team of trained reviewers and each entry was validated by at least one secondary reviewer. We used logistic regression models to assess whether the direction of shift supported common range-shift expectations (i.e., shifts to higher latitudes and elevations, and deeper depths). We also estimated the magnitude of shifts for the subset of available range-shift data expressed in distance per time (i.e., km/decade). We accounted for methodological attributes at the study level as potential sources of variation. This allowed us to answer two questions: (1) are most species shifting in the direction we expect (i.e., each observation is assessed as support/fail to support our expectation); and (2) what is the average speed of range shifts? Review findings We found that less than half of all range-shift observations (46.60%) documented shifts towards higher latitudes, higher elevations, and greater marine depths, demonstrating significant variation in the empirical evidence for general range shift expectations. For the subset of studies looking at range shift rates, we found that species demonstrated significant average shifts towards higher latitudes (average = 11.8 km/dec) and higher elevations (average = 9 m/dec), although we failed to find significant evidence for shifts to greater marine depths. We found that methodological factors in individual range-shift studies had a significant impact on the reported direction and magnitude of shifts. Finally, we identified important variation across dimensions of range shifts (e.g., greater support for latitude and elevation shifts than depth), parameters (e.g., leading edge shifts faster than trailing edge for latitude), and taxonomic groups (e.g., faster latitudinal shifts for insects than plants). Conclusions Despite growing evidence that species are shifting their ranges in response to climate change, substantial variation exists in the extent to which definitively empirical observations confirm these expectations. Even though on average, rates of shift show significant movement to higher elevations and latitudes for many taxa, most species are not shifting in expected directions. Variation across dimensions and parameters of range shifts, as well as differences across taxonomic groups and variation driven by methodological factors, should be considered when assessing overall confidence in range-shift hypotheses. In order for managers to effectively plan for species redistribution, we need to better account for and predict which species will shift and by how much. The dataset produced for this analysis can be used for future research to explore additional hypotheses to better understand species range shifts.

Journal of Environmental Evidence↗