Environmental Data Sources Research Articles

Landscape analysis of environmental data sources for linkage with SEER cancer patients database.

One of the challenges associated with understanding environmental impacts on cancer risk and outcomes is estimating potential exposures of individuals diagnosed with cancer to adverse environmental conditions over the life course. Historically, this has been partly due to the lack of reliable measures of cancer patients' potential environmental exposures before a cancer diagnosis. The emerging sources of cancer-related spatiotemporal environmental data and residential history information, coupled with novel technologies for data extraction and linkage, present an opportunity to integrate these data into the existing cancer surveillance data infrastructure, thereby facilitating more comprehensive assessment of cancer risk and outcomes. In this paper, we performed a landscape analysis of the available environmental data sources that could be linked to historical residential address information of cancer patients' records collected by the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. The objective is to enable researchers to use these data to assess potential exposures at the time of cancer initiation through the time of diagnosis and even after diagnosis. The paper addresses the challenges associated with data collection and completeness at various spatial and temporal scales, as well as opportunities and directions for future research.

Data Linkages for Wildfire Exposures and Human Health Studies: A Scoping Review.

Wildfires are increasing in frequency and intensity, with significant consequences that impact human health. A scoping review was conducted to: (a) understand wildfire-related health effects, (b) identify and describe environmental exposure and health outcome data sources used to research the impacts of wildfire exposures on health, and (c) identify gaps and opportunities to leverage exposure and health data to advance research. A literature search was conducted in PubMed and a sample of 83 articles met inclusion criteria. A majority of studies focused on respiratory and cardiovascular outcomes. Hospital administrative data was the most common health data source, followed by government data sources and health surveys. Wildfire smoke, specifically fine particulate matter (PM2.5), was the most common exposure measure and was predominantly estimated from monitoring networks and satellite data. Health data were not available in real-time, and they lacked spatial and temporal coverage to study health outcomes with longer latency periods. Exposure data were often available in real-time and provided better temporal and spatial coverage but did not capture the complex mixture of hazardous wildfire smoke pollutants nor exposures associated with non-air pathways such as soil, household dust, food, and water. This scoping review of the specific health and exposure data sources used to underpin these studies provides a framework for the research community to understand: (a) the use and value of various environmental and health data sources, and (b) the opportunities for improving data collection, integration, and accessibility to help inform our understanding of wildfires and other environmental exposures.

Strengthening Partnerships to Safeguard the Future of Herbaria

Herbaria remain the primary means of documenting plant life on earth, and the number of herbaria worldwide and the number of specimens they hold continues to grow. Digitization of herbarium specimens, though far from complete, has increased the discoverability of herbarium holdings and has increased the range of studies from which data from herbarium specimens can be used. The rather large number of herbaria about which no current information is available is a source of concern, as is herbarium consolidation and removal of herbaria to offsite storage facilities. Partnerships are key to the future health of herbaria. Benefits could accrue from the reimagining of the world’s herbaria as a global resource rather than a collection of independent, often competing institutions. Herbaria can extend the reach of their specimens by joining the nascent effort to link the species occurrence data they manage to other biological and environmental data sources to deepen our ability to understand the interrelationships of earth’s biota. To assure that data held by herbaria contribute to the range of conservation-related projects for which they are relevant, herbaria should embrace the tenets of Team Science and play a more proactive role in promoting their holdings for relevant research and conservation projects.

Utilizing machine learning algorithm for finely three-dimensional delineation of soil-groundwater contamination in a typical industrial park, North China: Importance of multisource auxiliary data

Intensive industrial activities cause soil contamination with wide variations and even perturb groundwater safety. Precision delineation of soil contamination is the foundation and precondition for soil quality assurance in the practical environmental management process. However, spatial non-stationarity phenomenon of soil contamination and heterogeneous sampling are two key issues that affect the accuracy of contamination delineation model. Taking a typical industrial park in North China as the research object, we constructed a random forest (RF) model for finely characterizing the distribution of soil contaminants using sparse-biased drilling data. Results showed that the R2 values of arsenic and 1,2-dichloroethane predicted by RF (0.8896 and 0.8973) were greatly higher than those of inverse distance weighted model (0.2848 and 0.2908), indicating that RF was more adaptable to actual non-stationarity sites. The back propagation neural network algorithm was utilized to establish a three-dimensional visualization of the contamination parcel of subsoil-groundwater system. Multiple sources of environmental data, including hydrogeological conditions, geochemical characteristics and anthropogenic industrial activities were integrated into the model to optimize the prediction accuracy. The feature importance analysis revealed that soil particle size was dominant for the migration of arsenic, while the migration of 1,2-dichloroethane highly depended on vertical permeability coefficients of the soil. Contaminants migrated downwards with soil water under gravity-driven conditions and penetrated through the subsoil to reach the saturated aquifer, forming a contamination plume with groundwater flow. Our findings afford a new idea for spatial analysis of soil-groundwater contamination at industrial sites, which will provide valuable technical support for maintaining sustainable industry.

IoT-based wireless data acquisition and control system for photovoltaic module performance analysis

The development of photovoltaic (PV) technology has led to an increasing demand for efficient and reliable monitoring systems that can ensure the optimal performance of PV modules. In particular, remote monitoring systems are critical for PV systems installed in distant locations where manual monitoring is difficult and expensive. In this article, we introduce a low-cost wireless monitoring system that employs NodeMCU boards, Raspberry Pi, and Internet of Things (IoT) technologies to monitor and analyze the operational and environmental data of PV modules. By using free and open-source software packages including MQTT Mosquitto Broker, InfluxDB, Node-RED, and Grafana, the system can collect, store, and visualize data, offering an effective means of studying a PV system performance from anywhere. The proposed system is found to work efficiently under different testing conditions; providing precise measurements for different parameters, including voltage, current, power, ambient temperature, humidity, panel temperature, PV coolant temperature at inlet and outlet, and spectral irradiance. In addition to the data collected by the set of sensors, the system provides another source of environmental data using a web-based service called "OpenWeatherMap" that provides a complete global forecast. Alerts and thresholds have been successfully implemented in our system, providing an added advantage of being able to detect and respond to any anomalies in the collected data. Furthermore, the system provides two operational controls: wireless ON/OFF control and wireless control of a servo motor which can be utilized in various applications improving overall system performance.

Linking coastal environmental and health observations for human wellbeing.

Coastal areas have long been attractive places to live, work, and recreate and remain so even in the face of growing threats from global environmental change. At any moment, a significant portion of the human population is exposed to both positive and negative health effects associated with coastal locations. Some locations may be "hotspots" of concern for human health due to ongoing climatic and other changes, accentuating the need for better understanding of coastal environment-human health linkages. This paper describes how environmental and health data could be combined to create a coastal environmental and human health observing system. While largely based on information from the US and Europe, the concept should be relevant to almost any coastal area. If implemented, a coastal health observing system would connect a variety of human health data and environmental observations for individuals and communities, and where possible cohorts. Health data would be derived from questionnaires and other personal sources, clinical examinations, electronic health records, wearable devices, and syndromic surveillance, plus information on vulnerability and health-relevant community characteristics, and social media observations. Environmental data sources would include weather and climate, beach and coastal conditions, sentinel species, occurrences of harmful organisms and substances, seafood safety advisories, and distribution, proximity, and characteristics of health-promoting green and blue spaces. Where available, information on supporting resources could be added. Establishment of a linked network of coastal health observatories could provide powerful tools for understanding the positive and negative health effects of coastal living, lead to better health protections and enhanced wellbeing, and provide significant benefits to coastal residents, including the historically disadvantaged, as well as the military, hospitals and emergency departments, academic medical, public health, and environmental health programs, and others. Early networks could provide best practices and lessons learned to assist later entries.

Hyperlocal environmental data with a mobile platform in urban environments

Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking.

Valuing the Quaternary – Nature conservation and geoheritage

This paper introduces the Special Issue of the Proceedings of the Geologists' Association on ‘Valuing the Quaternary – Nature Conservation and Geoheritage’, arising from the International Union for Quaternary Research (INQUA) Congress in Dublin, in July 2019. It presents an overview of the values of Quaternary geoheritage, which merit recognition as an integral part of nature conservation, to protect priority sites and features for scientific research and education, and to deliver wider ecological, cultural and aesthetic benefits. The paper highlights the benefits of incorporating knowledge and understanding of Quaternary geoheritage for nature conservation and society. Palaeoenvironmental, palaeoecological and palaeobiological archives are a key source of ecological and environmental data that allow learning from the past to help address contemporary conservation challenges such as biodiversity loss, anthropogenic pressures and climate change. Quaternary science plays a vital part in supporting the wider nature conservation agenda, including strengthening the role of protected and conserved areas in the sustainable management of natural capital and ecosystem services, climate change adaptation, marine conservation, nature restoration and recovery, connecting people and nature and informing nature-based solutions to threats faced by society. However, challenges remain to achieve protection of key geoheritage sites and landscapes globally, and to integrate better understanding of geodiversity in nature conservation research, policy development and practice to help address the twin crises facing nature conservation – biodiversity loss and climate change. Quaternary studies provide temporal and spatial perspectives to inform forward-looking nature conservation that is dynamic rather than static in outlook.

Automation of tree‐ring detection and measurements using deep learning

Abstract Core samples from trees are a critical reservoir of ecological information, informing our understanding of past climates, as well as contemporary ecosystem responses to global change. Manual measurements of annual growth rings in trees are slow, labour‐intensive and subject to human bias, hindering the generation of big datasets. We present an alternative, neural network‐based implementation that automates detection and measurement of tree‐ring boundaries from coniferous species. We trained our Mask R‐CNN extensively on over 8000 manually annotated ring boundaries from microscope‐imaged Norway Spruce Picea abies increment cores. We assessed the performance of the trained model after post‐processing on real‐world data generated from our core processing pipeline. The CNN after post‐processing performed well, with recognition of over 98% of ring boundaries (recall) with a precision in detection of 96% when tested on real‐world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were 98% correlated with human measurements of the same samples. Tests on other three conifer species demonstrate that the CNN generalizes well to other species with similar structure. We demonstrate the efficacy of automating the measurement of growth increment in tree core samples. Our CNN‐based system provides high predictive performance in terms of both tree‐ring detection and growth rate determination. Our application is readily deployable as a Docker container and requires only basic command line skills. Additionally, an easy re‐training option allows users to expand capabilities to other wood types. Application outputs include both editable annotations of predictions as well as ring‐width measurements in a commonly used .pos format, facilitating the efficient generation of large ring‐width measurement datasets from increment core samples, an important source of environmental data.

Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic

An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.

New strategic framework to structure Cumulative Impact Assessment (CIA)

In order to alleviate climate change consequences, UK governments are pioneering offshore energy developments with increasing commitment. The North Sea is a dynamic ecosystem with strong bottom-up/top-down natural and anthropogenic drivers facing rapid climate change impacts. Therefore, to ensure the compatibility of such large-scale developments with nature conservation obligations, cumulative effects need to be evaluated through cumulative impact assessments (CIA). However, by excluding climate change impacts, CIA lacks spatio-temporal appropriate baselines linking ecosystem components (e.g. physical indicators) to population dynamics which leads to uncertain predictions at populations levels. This study presents an overview of a framework for CIA using a holistic and pragmatic ecosystem approach based on spatio-temporal Bayesian network in order to identify pressure pathways, keystone components, ecosystem connectivity and resilience as well as population-level changes. We will also present potential fine-scale environmental monitoring solutions and data sources generated at MRED (Marine Renewable Energy Developments) site levels. Finally, we will discuss the usefulness of the two components that make up this framework: a database and an application of standardised shared tools that will pave the way to more transparent and multi-disciplinary collaborations. This framework will provide a multi-dimensional decision-making toolkit that would also lead towards more efficient SEAs (Strategic Environmental Assessment) as well as providing the ability to embed the CIAs of projects into regional and multinational schemes.

Environmental Monitoring for Arctic Resiliency and Sustainability: An Integrated Approach with Topic Modeling and Network Analysis

The Arctic environment is experiencing profound and rapid changes that will have far-reaching implications for resilient and sustainable development at the local and global levels. To achieve sustainable Arctic futures, it is critical to equip policymakers and global and regional stake- and rights-holders with knowledge and data regarding the ongoing changes in the Arctic environment. Community monitoring is an important source of environmental data in the Arctic but this research argues that community-generated data are under-utilized in the literature. A key challenge to leveraging community-based Arctic environmental monitoring is that it often takes the form of large, unstructured data consisting of field documents, media reports, and transcripts of oral histories. In this study, we integrated two computational approaches—topic modeling and network analysis—to identify environmental changes and their implications for resilience and sustainability in the Arctic. Using data from community monitoring reports of unusual environmental events in the Arctic that span a decade, we identified clusters of environmental challenges: permafrost thawing, infrastructure degradation, animal populations, and fluctuations in energy supply, among others. Leveraging visualization and analytical techniques from network science, we further identified the evolution of environmental challenges over time and contributing factors to the interconnections between these challenges. The study concludes by discussing practical and methodological contributions to Arctic resiliency and sustainability.

To the question of creating an open ecological geographic information system

Access to information about the state of the environment is an inalienable right of Ukrainian citizens, so the development and improvement of ways to provide information to the population with environmental data is of high social importance. A valuable source of reliable environmental data that can be used to inform the public are the results of scientific research on the state of the environment, which are produced by the activities of higher education institutions. This article considers the possibilities of using web-GIS services by higher education institutions as a means of communicating environmental information to the general public. Purpose. To create an open geographic information system that should represent the database of environmental geodata obtained in the process of educational and scientific work of students and staff of the Karazin Institute of Environmental Sciences. Methods. Analysis of the stock materials of the Institute of Ecology for the needs of forming a geodatabase, development of the structure of the attributive web-GIS database and testing the functionality of the ArcGIS Online cloud mapping platform service for exporting, updating, storing, searching and visualizing data presentation in web-GIS. Results. A methodical algorithm for the development and creation of web-GIS is proposed and tested, consisting of the following stages: 1) selection of the technological base of development; 2) analysis of materials providing input information; 3) development of the structure of the attributive database of the project; 4) preparation of geodata sets and their publication on the Internet. As a result of the analysis 144 scientific papers were selected, which will form the information basis for the formation of the web-GIS "EcoSpace". The structure of the attributive database of the web-GIS project was developed, consisting of 4 content blocks and 11 types of descriptive data. 3 sets of test data were prepared, which showed successful results of export and visualization of geodata represented by different types of geometric vector primitives (linear, point and polygonal objects). Conclusions. A prototype of the web-GIS "EcoSpace" (https://institute-of-environmental-sciences-data-portal-carto-lab.hub.arcgis.com/) was developed in the environment of the ArcGIS Online Internet service, which can be used to publish information about the state of the environment contained in the results of scientific research of students and staff of the Karazin Institute of Environmental Sciences. The information content of the web-GIS "EcoSpace" has been started, in particular, the results of three scientific projects concerning the state of soil resources of Kharkiv district of Kharkiv region have been entered.

Neighborhood environmental factors linked to hospitalizations of older people for viral lower respiratory tract infections in Spain: a case-crossover study

BackgroundLower respiratory tract viral infection (LRTI) is a significant cause of morbidity-mortality in older people worldwide. We analyzed the association between short-term exposure to environmental factors (climatic factors and outdoor air pollution) and hospital admissions with a viral LRTI diagnosis in older adults.MethodsWe conducted a bidirectional case-crossover study in 6367 patients over 65 years of age with viral LRTI and residential zip code in the Spanish Minimum Basic Data Set. Spain’s State Meteorological Agency was the source of environmental data. Associations were assessed using conditional logistic regression. P-values were corrected for false discovery rate (q-values).ResultsAlmost all were hospital emergency admissions (98.13%), 18.64% were admitted to the intensive care unit (ICU), and 7.44% died. The most frequent clinical discharge diagnosis was influenza (90.25%). LRTI hospital admissions were more frequent when there were lower values of temperature and O3 and higher values of relative humidity and NO2. The regression analysis adjusted by temperatures and relative humidity showed higher concentrations at the hospital admission for NO2 [compared to the lag time of 1-week (q-value< 0.001) and 2-weeks (q-value< 0.001)] and O3 [compared to the lag time of 3-days (q-value< 0.001), 1-week (q-value< 0.001), and 2-weeks (q-value< 0.001)] were related to a higher odds of hospital admissions due to viral LRTI. Moreover, higher concentrations of PM10 at the lag time of 1-week (q-value = 0.023) and 2-weeks (q-value = 0.002), and CO at the lag time of 3-days (q-value = 0.023), 1-week (q-value< 0.001) and 2-weeks (q-value< 0.001)], compared to the day of hospitalization, were related to a higher chances of hospital admissions with viral LRTI.ConclusionUnfavorable environmental factors (low temperatures, high relative humidity, and high concentrations of NO2, O3, PM10, and CO) increased the odds of hospital admissions with viral LRTI among older people, indicating they are potentially vulnerable to these environmental factors.Graphical

Satellite remote sensing of environmental variables can predict acoustic activity of an orthopteran assemblage.

Passive acoustic monitoring (PAM) is a promising method for biodiversity assessment, which allows for longer and less intrusive sampling when compared to traditional methods (e.g., collecting specimens), by using sound recordings as the primary data source. Insects have great potential as models for the study and monitoring of acoustic assemblages due to their sensitivity to environmental changes. Nevertheless, ecoacoustic studies focused on insects are still scarce when compared to more charismatic groups. Insects’ acoustic activity patterns respond to environmental factors, like temperature, moonlight, and precipitation, but community acoustic perspectives have been barely explored. Here, we provide an example of the usefulness of PAM to track temporal patterns of acoustic activity for a nocturnal assemblage of insects (Orthoptera). We integrate satellite remote sensing and astronomically measured environmental factors at a local scale in an Andean Forest of Colombia and evaluate the acoustic response of orthopterans through automated model detections of their songs for nine weeks (March and April of 2020). We describe the acoustic frequency range and diel period for the calling song of each representative species. Three species overlapped in frequency and diel acoustics but inhabit different strata: canopy, understory, and ground surface level. Based on the acoustic frequency and activity, we identified three trends: (i) both sampled cricket species call at lower frequency for shorter periods of time (dusk); (ii) all sampled katydid species call at higher frequency for longer time periods, including later hours at night; and (iii) the diel acoustic activity span window seems to increase proportionally with dominant acoustic frequency, but further research is required. We also identified a dusk chorus in which all the species sing at the same time. To quantify the acoustic response to environmental factors, we calculated a beta regression with the singing activity as a response variable and moon phase, surface temperature and daily precipitation as explanatory variables. The response to the moon phase was significant for the katydids but not for the crickets, possibly due to differences in diel activity periods. Crickets are active during dusk, thus the effects of moonlight on acoustic activity are negligible. The response to precipitation was significant for the two crickets and not for the katydids, possibly because of higher likelihood of rain interrupting crickets’ shorter diel activity period. Our study shows how the local survey of orthopteran acoustic assemblages, with a species taxonomic resolution coupled with remote-sensing environmental measurements can reveal responses to environmental factors. In addition, we demonstrate how satellite data might prove to be a useful alternative source of environmental data for community studies with geographical, financial, or other constraints.

Planning for community well-being: Prioritizing and identifying local neighbourhood attributes of belonging.

ObjectivesLocal neighbourhoods have great potential to foster community belonging which can improve health and well-being. In partnership with the City of Toronto, we prioritize and assess the importance of physical and social environmental attributes to community belonging. Using new data linkages, we contribute to Toronto’s community safety and well-being plan, SafeTO.&#x0D; ApproachWe leverage an individual-level record linkage of respondents from multiple cycles of the Canadian Community Health Survey (CCHS, 2000 to 2017), the Canadian Vital Statistics Death Database (CVSD), and the Discharge Abstract Database (DAD) from the Centre for Population Health Data at Statistics Canada. Environmental data sources include the Census, administrative data, and open-source data. Using postal code information from the CCHS, we connected each respondent with their neighbourhood’s attributes, which include but are not limited to proximity to amenities (such as childcare, libraries and public transit), the Canadian Active Living Environment measure, green space, and air quality.&#x0D; ResultsOf the 74,000 CCHS respondents from the Toronto census metropolitan area (representing a population of 4 to 5 million annually), an overall rate of 86% agreed to link and share their data, which renders an estimated linked sample of approximately 63,600 respondents. Across the entire linkage, 54% of respondents are linked to a record in the DAD between 1999/00 and 2017/18, and 10% are linked to a death record in the CVSD between 2000 and 2017. In consultation with municipal agencies and community stakeholders, we will prioritize the environmental attributes that are most relevant to community belonging and well-being, and comprehensively model the relationship between these attributes, community belonging and downstream health outcomes using multivariable regression and time-to-event models.&#x0D; ConclusionThis on-going work contributes to the development of innovative approaches for using multi-sector data to inform decision making, as per the goals of SafeTO. Results from the analyses will be used to identify environmental attributes potentially important for community belonging and will support municipal city planning and resource allocation.

Whale-Associated Microbial Communities Remain Remarkably Stable despite Massive Water Community Disruption in a Managed Artificial Marine Environment

Highly managed and built environments such as zoos and aquaria provide a rich source of standardized environmental monitoring data over periods of years to decades. A fifty percent water change in an 11.4-million-liter indoor artificial sea water system housing three species of marine mammals was conducted over a two-month period. Using 16S rRNA gene sequencing, the microbial community structure of the system water and three host sites (feces, skin, and exhaled breath “chuff”) of whales housed in the system were characterized. Diversity measures confirmed massive disruption to the water community structure as an expected result of the water change. Host site-associated communities remained remarkably stable. Improved understanding of host microbial community dynamics in response to environmental system perturbations allows for sound management decisions toward optimizing conditions for resident animals.

A Scoping Review of Species Distribution Modeling Methods for Tick Vectors

BackgroundGlobally, tick-borne disease is a pervasive and worsening problem that impacts human and domestic animal health, livelihoods, and numerous economies. Species distribution models are useful tools to help address these issues, but many different modeling approaches and environmental data sources exist.ObjectiveWe conducted a scoping review that examined all available research employing species distribution models to predict occurrence and map tick species to understand the diversity of model strategies, environmental predictors, tick data sources, frequency of climate projects of tick ranges, and types of model validation methods.DesignFollowing the PRISMA-ScR checklist, we searched scientific databases for eligible articles, their references, and explored related publications through a graphical tool (www.connectedpapers.com). Two independent reviewers performed article selection and characterization using a priori criteria.ResultsWe describe data collected from 107 peer-reviewed articles that met our inclusion criteria. The literature reflects that tick species distributions have been modeled predominantly in North America and Europe and have mostly modeled the habitat suitability for Ixodes ricinus (n = 23; 21.5%). A wide range of bioclimatic databases and other environmental correlates were utilized among models, but the WorldClim database and its bioclimatic variables 1–19 appeared in 60 (56%) papers. The most frequently chosen modeling approach was MaxEnt, which also appeared in 60 (56%) of papers. Despite the importance of ensemble modeling to reduce bias, only 23 papers (21.5%) employed more than one algorithm, and just six (5.6%) used an ensemble approach that incorporated at least five different modeling methods for comparison. Area under the curve/receiver operating characteristic was the most frequently reported model validation method, utilized in nearly all (98.9%) included studies. Only 21% of papers used future climate scenarios to predict tick range expansion or contraction. Regardless of the representative concentration pathway, six of seven genera were expected to both expand and retract depending on location, while Ornithodoros was predicted to only expand beyond its current range.ConclusionSpecies distribution modeling techniques are useful and widely employed tools for predicting tick habitat suitability and range movement. However, the vast array of methods, data sources, and validation strategies within the SDM literature support the need for standardized protocols for species distribution and ecological niche modeling for tick vectors.

Northern plains late precontact and historic period winter sand dune usage by bison and human populations

In past examinations of bison and human seasonal migration on the Northern Plains, ecology has played a central role. The definition of ecological regions, however, has only recognized the presence of either parkland or grassland regions. While some works do recognize the small role of “anomalous” landscapes within the grassland such as sand dunes, no detailed examination of these areas has yet to be conducted. This study approaches the role that dune environments played on the Northern Plains to present a holistic interpretation of environmental, historical, and archaeological data sources, and questions their classification as being anomalous. This research concludes that the present and continued use of broad-based ecological classifications to answer questions on bison and human landscape usage do not adequately or accurately interpret the body of historical and archaeological data currently on hand.

Aging Hearts in a Hotter, More Turbulent World: The Impacts of Climate Change on the Cardiovascular Health of Older Adults.

Purpose of ReviewClimate change has manifested itself in multiple environmental hazards to human health. Older adults and those living with cardiovascular diseases are particularly susceptible to poor outcomes due to unique social, economic, and physiologic vulnerabilities. This review aims to summarize those vulnerabilities and the resultant impacts of climate-mediated disasters on the heart health of the aging population.Recent FindingsAnalyses incorporating a wide variety of environmental data sources have identified increases in cardiovascular risk factors, hospitalizations, and mortality from intensified air pollution, wildfires, heat waves, extreme weather events, rising sea levels, and pandemic disease. Older adults, especially those of low socioeconomic status or belonging to ethnic minority groups, bear a disproportionate health burden from these hazards.SummaryThe worldwide trends responsible for global warming continue to worsen climate change–mediated natural disasters. As such, additional investigation will be necessary to develop personal and policy-level interventions to protect the cardiovascular wellbeing of our aging population.