Large Geographic Regions Research Articles

Deep sediment heterogeneity inferred using very low-frequency features from merchant shipsa).

The very low-frequency noise from merchant ships provides a good broadband sound source to study the deep layers of the seabed. The nested striations that characterize ship time-frequency spectrograms contain unique acoustic features corresponding to where the waveguide invariant β becomes infinite. In this dataset, these features occur at frequencies between 20 and 80 Hz, where pairs of modal group velocities become equal. The goal of this study is to identify these β = ∞ frequencies in ship noise spectrograms and use them to perform statistical inference for the deep layer sound speeds and thicknesses in the New England Mudpatch for a larger number of ships and acoustic arrays over a larger geographical region than previously studied. Marginal probability distributions of the data indicate that using singular points for a feature-based inversion yields an estimate of the sound speed and a limiting value for the thickness of the first deep layer. Heterogeneity is examined by correlating spatial variability of the deep layer sound speeds with ship tracks.

Hazard-consistent scenario selection for long-term storm surge risk assessment over extended coastal regions

The recent destructive hurricane seasons and concerns related to the future influence of climate change have increased the relevance of coastal storm hazards and, in particular, of storm surge hazard estimation when discussing the resilience of coastal communities. This hazard is generally represented as surge inundation probabilities over a large number of individual locations in the geographic domain of interest, and is typically assessed utilizing an ensemble of storm scenarios (i.e., storm events) that are representative of the regional climatology. This paper investigates the storm ensemble selection within this setting, with the objective of identifying a small number of storm scenarios that are consistent with some chosen hazard descriptions over a large geographic region. Beyond the storm events, the occurrence rates (i.e., weights) are also updated. Following past works, a two-stage optimization is adopted for the storm selection. The inner-loop identifies the occurrence rates for a given storm subset, formulating the problem as a linear programming optimization for the sum of absolute deviation for the predicted hazard. The outer-loop searches for the best subset with the desired number of storms, adopting a genetic algorithm integer optimization for minimizing the aforementioned deviation. This work extends this implementation to extended coastal regions, with many locations of interest. In this case, it is computationally intractable to consider all the locations in the domain within the linear programming formulation, and for this reason, a subset of representative locations is chosen through cluster analysis. The hazard description for only these locations is used in the storm ensemble selection. For clustering, different strategies using correlation between locations based on geospatial information, surge response, or a combination of both are examined. Additionally, the correlation in the hazard description is better integrated into the storm selection by establishing a modification of the objective function adopted for the outer optimization loop. Applications to different North Atlantic coastal domains are presented as case studies.

Spillover effects from a type 2 diabetes integrated model of care in 22,706 Australians: an open cohort stepped wedge trial

BackgroundMany Australian adults are not receiving timely or effective diabetes management to prevent or delay the onset of diabetes related complications. Integrated care, a worldwide trend in healthcare reform, aims to reduce the fragmented delivery of health services and improve outcomes. This study aimed to test whether a specialist-led integrated model of care provided to a small subset of patients in general practices leads to spillover clinical improvements in all patients of the practice with type 2 diabetes.MethodsSeventy-two general practice sites (clusters) in New South Wales, Australia received the Diabetes Alliance intervention, creating a non-randomised open cohort stepped wedge trial. The intervention comprised of case conferencing, delivered directly to a small proportion of adults with type 2 diabetes (n = 1,072) of the general practice sites; as well as practice feedback, education and training. Spillover clinical improvements were assessed on all adults with type 2 diabetes within the general practice sites (n = 22,706), using practice level data recorded in the MedicineInsight electronic database, compared before and after the intervention. Outcome measures included frequency of diabetes screening tests in line with the Annual Cycle of Care, and clinical results for weight, blood pressure, HbA1c, lipids, and kidney function.ResultsCompared to before Diabetes Alliance, the odds of all practice patients receiving screening tests at or above the recommended intervals were significantly higher for all recommended tests after Diabetes Alliance (odds ratio range 1.41–4.45, p < 0.0001). Significant improvements in clinical outcomes were observed for weight (absolute mean difference: -1.38 kg), blood pressure (systolic − 1.12 mmHg, diastolic − 1.18 mmHg), HbA1c (-0.03% at the mean), total cholesterol (-0.11 mmol/L), and triglycerides (-0.02 mmol/L) (p < 0.05). There were small but significant declines in kidney function.ConclusionsIntegrated care delivered to a small subset of patients with type 2 diabetes across a large geographic region has spillover benefits that improve the process measures and clinical outcomes for all practice patients with type 2 diabetes.Trial registrationACTRN12622001438741; 10th November 2022, retrospectively registered: https://www.anzctr.org.au/ACTRN12622001438741.aspx.

Training Dental Teams to Address Community Health: Necessary Partnerships and Evaluation of an Evidence-Based Curriculum

Abstract: Background: Annually, 27 million Americans visit a dental professional but not a physician. Dental professionals must recognize that they are members of their patients’ primary care teams. Continuing education must then prepare them to appropriately serve their specific communities. Objectives: The objective of this paper was to describe the implementation of an evidence-based model to train dental professionals on how to respond to community-level health needs. The paper details crucial partnerships and provides evidence and key considerations for replicating the curriculum to improve population health. Methods: The Extension for Community Healthcare Outcomes (ECHO) model was used in one state where dental health care use is challenging for persons living rural, eligible for Medicaid, aging, and those who are American Indian. This formative evaluation knowledge assessments, data on participants’ changes in clinical care practice, web analytics, and artifact review to assess effective implementation strategies and necessary community partnerships. Conclusions: Successful implementation of the curriculum required active participation and partnership with state provider associations, the office of Medicaid, the state Board of Dental Licensure, and others. Without engaged partners, the curriculum would not have been community relevant, nor would it have had case presentation from local providers. In a state with only 427 practicing dentists, live attendance ranged between 9 and 22 dental team members, with between 11 and 91 views of the recorded sessions. Using the evidence-based ECHO model, which requires community health partnerships, is a cost-effective, and accessible, method to offering community specific education for dental providers across a large geographic region.

Microscale Temperature-Humidity Index (THI) Distribution Estimated at the City Scale: A Case Study in Maebashi City, Gunma Prefecture, Japan

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development.

Comprehensive identification of genomic and environmental determinants of phenotypic plasticity in maize.

Maize phenotypes are plastic, determined by the complex interplay of genetics and environmental variables. Uncovering the genes responsible and understanding how their effects change across a large geographic region are challenging. In this study, we conducted systematic analysis to identify environmental indices that strongly influence 19 traits (including flowering time, plant architecture, and yield component traits) measured in the maize nested association mapping (NAM) population grown in 11 environments. Identified environmental indices based on day length, temperature, moisture, and combinations of these are biologically meaningful. Next, we leveraged a total of more than 20 million SNP and SV markers derived from recent de novo sequencing of the NAM founders for trait prediction and dissection. When combined with identified environmental indices, genomic prediction enables accurate performance predictions. Genome-wide association studies (GWASs) detected genetic loci associated with the plastic response to the identified environmental indices for all examined traits. By systematically uncovering the major environmental and genomic factors underlying phenotypic plasticity in a wide variety of traits and depositing our results as a track on the MaizeGDB genome browser, we provide a community resource as well as a comprehensive analytical framework to facilitate continuing complex trait dissection and prediction in maize and other crops. Our findings also provide a conceptual framework for the genetic architecture of phenotypic plasticity by accommodating two alternative models, regulatory gene model and allelic sensitivity model, as special cases of a continuum.

Rodents consuming the same toxic diet harbor a unique functional core microbiome

Gut microbiota are intrinsic to an herbivorous lifestyle, but very little is known about how plant secondary compounds (PSCs), which are often toxic, influence these symbiotic partners. Here we interrogated the possibility of unique functional core microbiomes in populations of two species of woodrat (Neotoma lepida and bryanti) that have independently converged to feed on the same toxic diet (creosote bush; Larrea tridentata) and compared them to populations that do not feed on creosote bush. Leveraging this natural experiment, we collected samples across a large geographic region in the U.S. desert southwest from 20 populations (~ 150 individuals) with differential ingestion of creosote bush and analyzed three gut regions (foregut, cecum, hindgut) using16S sequencing and shotgun metagenomics. In each gut region sampled, we found a distinctive set of microbes in individuals feeding on creosote bush that were more abundant than other ASVs, enriched in creosote feeding woodrats, and occurred more frequently than would be predicted by chance. Creosote core members were from microbial families e.g., Eggerthellaceae, known to metabolize plant secondary compounds and three of the identified core KEGG orthologs (4-hydroxybenzoate decarboxylase, benzoyl-CoA reductase subunit B, and 2-pyrone-4, 6-dicarboxylate lactonase) coded for enzymes that play important roles in metabolism of plant secondary compounds. The results support the hypothesis that the ingestion of creosote bush sculpts the microbiome across all major gut regions to select for functional characteristics associated with the degradation of the PSCs in this unique diet.

Latitudinal patterns of shifts in cladoceran communities throughout the Holocene: A paleoecological case study of northwestern Russia

Paleolimnology is a rapidly expanding field within the natural sciences. To date, many scientific papers have been published focusing on studies of remains from single sediment cores in specific lakes, or a few cores from the same water body. However, few previous publications have provided a comprehensive comparative analysis of taphocenosis (and maternal community) changes in different water bodies from a large geographical region during whole Holocene using multivariate statistical methods. We conducted a joined statistical analysis of data obtained as a result of the cladoceran analysis of the sediment samples from six cores taken along the North-South transect from the Kola Peninsula to the Karelian Region (North-West Russia). Our aim was to identify, based on quantitative statistical methods, general whole-Holocene patterns in cladoceran taphocenosis changes. All taphocenoses in all cores from six lakes can be classified into six clusters based on their species composition. Our unexpected conclusion is that different taphocoenoses are formed by the same set of species, but their proportions vary significantly. This suggests that the same species may play different functional roles in different maternal communities. During whole Holocene, we observed a single taphocenosis cluster in the Holocene core from Lake Antyukh-Lambina, and four taphocenosis clusters in the Lake Medvedevskoe, with an intermediate number of clusters in the lakes between them. Furthermore, it can be asserted that the potential for cluster changes in the core is greater for the more southern studied water bodies (0–3 clusters for northern lakes vs. 4 clusters for southern lakes). Based on our data, we hypothesize that the diversity of cladoceran associations during the Holocene was lower in Arctic/Subarctic lakes, possibly due to their formation by ubiquitous species. Our study suggests that at least some Arctic lake ecosystems have remained unchanged since deglaciation times. Further studies are necessary to determine if this is a general rule.

Stratospheric air intrusions promote global-scale new particle formation.

New particle formation in the free troposphere is a major source of cloud condensation nuclei globally. The prevailing view is that in the free troposphere, new particles are formed predominantly in convective cloud outflows. We present another mechanism using global observations. We find that during stratospheric air intrusion events, the mixing of descending ozone-rich stratospheric air with more moist free tropospheric background results in elevated hydroxyl radical (OH) concentrations. Such mixing is most prevalent near the tropopause where the sulfur dioxide (SO2) mixing ratios are high. The combination of elevated SO2 and OH levels leads to enhanced sulfuric acid concentrations, promoting particle formation. Such new particle formation occurs frequently and over large geographic regions, representing an important particle source in the midlatitude free troposphere.

Positive correlation between Ammospiza caudacuta (Saltmarsh Sparrow) capture and productivity supports use of a novel rapid assessment monitoring protocol

ABSTRACT Salt marshes in the northeastern United States provide critical breeding habitat for tidal marsh specialist birds like the Ammospiza caudacuta (Saltmarsh Sparrow). The Ammospiza caudacuta population declined by 9% annually from 1998 to 2012, necessitating immediate conservation actions for this vulnerable species. However, estimating species vital rates across a large geographic region is logistically challenging and cost prohibitive. Therefore, we developed and tested a rapid assessment monitoring protocol focused on reproductive metrics to enhance future conservation planning. We used 3 years (2018, 2019, and 2021) of intensive demographic data from 12 sites to estimate daily nest survival, nest period success, fledglings produced per female, and successful broods per female. We implemented the rapid assessment protocol co-located at intensive sites in the same years to estimate the number of captured females and juveniles. We used Pearson’s correlation analyses to determine the association of intensive metrics with rapid metrics. We found that the sum of Ammospiza caudacuta female and juvenile captures was positively correlated with daily nest survival (r = 0.61, P = 0.01), nest period success (r = 0.70, P = 0.002), fledglings produced per female (r = 0.82, P &amp;lt; 0.001), and successful broods per female (r = 0.82, P &amp;lt; 0.001). Our results demonstrate that fixed-effort mist-netting from our rapid assessment protocol is an informative and time-efficient sampling method that can aid in making informed management decisions related to Ammospiza caudacuta conservation.

Internet-Delivered Psychological Pain Management: A Prospective Cohort Study Examining Routine Care Delivery by a Specialist Regional Multidisciplinary Pain Service

Several clinical trials have demonstrated the effectiveness of internet-delivered psychological-based pain management programs (PMPs). However, to date, no large studies have reported the outcomes of PMPs when delivered by specialist multidisciplinary pain services in routine care. The present study reports (n = 653) the outcomes of an internet-delivered PMP provided as routine care by a specialist Australian regional pain service over a 6-year period. High levels of treatment commencement (85%) and completion (72%) were observed, with more than 80% of patients reporting they were satisfied with the intervention. Clinical improvements were observed from pretreatment to post-treatment (% change, 95% confidence intervals (CI)) in pain-related disability (8.8%; 4.5, 12.8), depression (28.4%; 23.0, 33.4), anxiety (21.9%; 14.6, 28.5), and pain intensity (7%; 3.5, 10.5), which were maintained to 3-month follow-up. At 3-month follow-up, 27% (23, 31), 46% (41, 51), 44% (39, 49), and 22% (19, 26) reported clinically meaningful (defined as ≥ 30%) improvements in pain-related disability, depression, anxiety, and pain intensity, respectively. These results were obtained with relatively little therapist time per patient (M = 30.0, (standard deviation) SD = 18.8) to deliver the intervention. The current findings highlight the potential of internet-delivered PMPs as part of the services provided by specialist pain services, particularly those servicing large geographical regions and for patients unable to travel to clinics for face-to-face care. PerspectiveThis study reports the outcomes of the routine delivery of an internet-delivered psychological PMP by a specialist pain service. The findings highlight the potential of this model of care when provided by specialist pain services, particularly for patients not unable to attend and not requiring intensive face-to-face care.

Genotype × environment interaction patterns of dry matter yield in meadow brome, orchardgrass, tall fescue, and timothy evaluated at harsh winter sites

AbstractBackgroundGenotype × environment interaction (GEI) slows genetic gains and complicates selection decisions in plant breeding programs. Forage breeding program seed sales often encompass large geographic regions to which the cultivars may not be adapted. An understanding of the extent of GEI in perennial, cool‐season forage grasses will facilitate improved selection decisions and end‐use in areas with harsh winters.MethodsWe evaluated the dry matter yield of nine meadow brome (Bromus biebersteinii Roemer &amp; J. A. Schultes), nine orchardgrass (Dactylis glomerata L.), seven tall fescue (Lolium arundinaceum (Schreb.) Darbysh.), and 10 timothy (Phleum pratense L.) cultivars or breeding populations at seven high latitude and/or elevation locations in Canada and the United States from 2019 to 2021.ResultsFor each of the species, we found significant differences among the genotypes for dry matter yield across environments and found significant levels of GEI. Using site regression analysis and GGE biplot visualizations, we then characterized the extent of the interactions in each species. Except for tall fescue, there was little evidence for the broad adaptation of genotypes across locations.ConclusionsThis research adds further evidence to the limitations of perennial, forage breeding programs to develop widely adapted cultivars and the need to maintain regional breeding efforts.

Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

A geographic information system approach for estimating state‐wide water quality credit need: Application for planned transportation projects in Virginia

AbstractUncertainty in water quality trading (WQT) markets is frequently cited as a deterrent for participation, with few studies focusing on uncertainty in future water quality credit needs. To reduce this uncertainty, we present a geographic information system (GIS)‐based methodology for estimating an upper bound of water quality credit needs for a set of spatially referenced planned construction projects over a large geographic region. We demonstrate the methodology by applying it to estimate future credit needs for the Virginia Department of Transportation's (VDOT) 6‐year improvement program. The results show that 25% of the state's 6‐digit hydrologic unit code (HUC) basins lack sufficient current credit supply to meet the estimated future credit need from VDOT's planned projects alone. Furthermore, while 70% of the 8‐digit HUCs containing planned projects have a sufficient current credit supply to meet VDOT credit needs, this is true for only 20% of the 10‐digit HUCs. Finally, nearly 25% of the planned transportation projects, representing potentially $9 million in credit purchases at current market rates, will be constructed in catchments with impaired water bodies. State regulations will initially limit these projects to trade with credit banks collocated at the 12‐digit HUC level. This application demonstrates how the GIS‐based methodology can be applied to reduce uncertainty about future WQT credit needs and how needs are aligned with current credit supply.

Harvest Timing of Standing Corn Using Near-Infrared Reflectance Spectroscopy.

Harvesting corn at the proper maturity is important for managing its nutritive value as livestock feed. Standing whole-plant moisture content is commonly utilized as a surrogate for corn maturity. However, sampling whole plants is time consuming and requires equipment not commonly found on farms. This study evaluated three methods of estimating standing moisture content. The most convenient and accurate approach involved predicting ear moisture using handheld near-infrared reflectance spectrometers and applying a previously established relationship to estimate whole-plant moisture from the ear moisture. The ear moisture model was developed using a partial least squares regression model in the 2021 growing season utilizing reference data from 610 corn plants. Ear moisture contents ranged from 26 to 80 %w.b., corresponding to a whole-plant moisture range of 55 to 81 %w.b. The model was evaluated with a validation dataset of 330 plants collected in a subsequent growing year. The model could predict whole-plant moisture in 2022 plants with a standard error of prediction of 2.7 and an R2P of 0.88. Additionally, the transfer of calibrations between three spectrometers was evaluated. This revealed significant spectrometer-to-spectrometer differences that could be mitigated by including more than one spectrometer in the calibration dataset. While this result shows promise for the method, further work should be conducted to establish calibration stability in a larger geographical region.

Lessons from a research partnership in southwest England to understand community palliative care needs in rural, coastal and low-income communities.

The South West Peninsula (Cornwall, Devon, Somerset) has the highest proportion of over 65s (24.2%) and is the only English rural population with greater economic deprivation than in urban areas. Coastal populations have the worst health outcomes in England. Despite innovation among communities to support those with health and care needs in later life, recruitment to palliative care research in the region is low and there has been no evaluation of public health palliative care interventions. A new South West Peninsula Palliative Care Research Partnership was funded for 15 months, bringing together four universities, the voluntary and community sector (including hospices) and local National Institute for Health and Care Research networks. The aim was to establish a sustainable multisectoral partnership that would identify community-based support needs for underserved rural and coastal populations by: • conducting a literature scoping review on interventions to enable community members to support the dying; • delivering a research capacity-building programme; • co-creating public and patient involvement capacity; • determining the resources and needs for communities to support dying well; • integrating findings to develop a framework of community-based support and identify future research questions; • establishing a sustainable research network infrastructure for the long-term design and delivery of palliative care research. We convened partners to identify research needs and co-designed activities to meet our objectives. These included a scoping review; a capacity-building programme of training, seminars and a journal club; forming a patient and public involvement group; a multistranded community engagement programme using different creative approaches; four focus groups with members of the public and one with community workers; and producing three 'storytelling' audio recordings. Findings were presented to the partnership at a regional workshop. The scoping review showed that community-engaged palliative care interventions can improve outcomes for individuals but provided little evidence about which approaches work for different communities. Five online seminars and a quarterly journal club to develop research capacity were regularly attended by 15 to 25 participants from across the partnership. While evaluating our engagement methods was beyond the partnership's scope, levels of participation suggested that creative methods of engagement show potential to help researchers and practitioners better understand the needs and priorities of underserved populations. Data showed that rural, coastal and low-income communities face challenges in accessing end-of-life care and support due to issues such as transport to and distance from services, erosion of neighbourhood networks, isolation from family and friends, 'patchiness' of palliative care services and a lack of care providers. Community organisations are well-placed to co-produce and facilitate methodologies for involving communities in palliative care research. Although activities took place in diverse areas, it was not possible within the available resources to cover the entire large geographical region, particularly the most isolated rural areas. Partnerships bringing together voluntary and community sector organisations, palliative care providers, health and social care providers, individuals with experience and academics have potential to design future research and public health interventions that better understand local context, involving and supporting communities to address their needs at end of life. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme as award number NIHR135312.

Body size estimation from isolated fossil bones reveals deep time evolutionary trends in North American lizards.

Lizards play vital roles in extant ecosystems. However, their roles in extinct ecosystems are poorly understood because the fossil record of lizards consists mostly of isolated bones. This makes it difficult to document changes in lizard morphology and body size over time, which is essential for studies of lizard paleoecology and evolution. It is also difficult to compare available fossil lizard data with existing sources of extant lizard data because extant studies rarely measure individual bones. Furthermore, no previous study has regressed measurements of individual bones to body length across crown lizard groups, nor tested those regressions on fossil skeletons. An extensive dataset of individual bone measurements from extant lizards across crown taxonomic groups is here employed to develop novel methods for estimating lizard body size from isolated fossil elements. These methods were applied to a comparably large dataset of fossil lizard specimens from the robust Paleogene record (66-23 Ma) of the Western Interior of North America. This study tests the hypothesis that anatomical proportions have been conserved within higher-level crown lizard groups since the Paleogene and can therefore be used to reconstruct snout-vent length (SVL) and mass for fossil specimens referred to the same groups. Individual bones demonstrated strong correlation with SVL in extant as well as fossil lizard specimens (R2 ≥ 0.69). Equations for mass estimation from individual bones were derived from the SVL regressions using published equations for calculating lizard body mass from SVL. The resulting body size estimates from regression equations for the entire fossil dataset revealed that lizards reached greatest maximum body size in the middle Paleogene, with the largest size class dominated by anguid lizards that exceeded 1 meter in SVL and 1.5 kg in body mass. Maximum body size decreased to under 400 mm and below 1.5 kg in the late Paleogene. No association was found between changes in maximum lizard body size and marine isotope proxies of global temperature through the Paleogene. This is the first study to investigate body size evolution across lizard clades over a deep time interval and for a large geographic region. The proposed methods can be used to generate body size regressions and provide estimates of body size for isolated lizard bones referred to any crown group.

Collaborative fisheries research reveals reserve size and age determine efficacy across a network of marine protected areas

AbstractA variety of criteria may influence the efficacy of networks of marine protected areas (MPA) designed to enhance biodiversity conservation and provide fisheries benefits. Meta‐analyses have evaluated the influence of MPA attributes on abundance, biomass, and size structure of harvested species, reporting that MPA size, age, depth, and connectivity influence the strength of MPA responses. However, few empirical MPA evaluation studies have used consistent sampling methodology across multiple MPAs and years. Our collaborative fisheries research program systematically sampled 12 no‐take or highly protective limited‐take MPAs and paired fished reference areas across a network spanning 1100 km of coastline to evaluate the factors driving MPA efficacy across a large geographic region. We found that increased size and age consistently contributed to increased fish catch, biomass, and positive species responses inside MPAs, while accounting for factors such as latitude, primary productivity, and distance to the nearest MPA. Our study provides a model framework to collaboratively engage diverse stakeholders in fisheries research and provide high‐quality data to assess the success of conservation strategies.

Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)

Concentrations of the chemical elements were analyzed in the shells of a bivalve mollusk species (Beringiana beringiana), water, and bottom sediments from seven lakes located on the Kamchatka Peninsula, the Kurile Islands, Sakhalin Island, and Primorsky Krai (Northeast Asia). A principal component analysis allowed to determine three factors those were related to environments in the waterbodies. We revealed two groups of samples corresponding to large geographical regions using the determined factors. Statistically significant differences were found between geographical groups of samples, and higher values of element distribution coefficients were determined for samples from lakes on the Kamchatka Peninsula. The highest concentrations of lithophilic elements were measured in the shells from Lake Kurazhechnoye (Kamchatka Peninsula). The highest concentrations of Al and Mg were detected in shells from Lake Peschanoye (Kunashir Island). In Lake Chernoye (Sakhalin Island), the highest concentrations of Sr and Sb in the shells were detected. Zn, Fe, Pb, and rare earth elements were present in large concentrations in the shells from Lake Vaskovskoye, Primorsky Krai. The shells of the Beringian freshwater mussel show large phenotypic plasticity, and their shape demonstrates significant relationships with various environmental parameters, that were assessed based on the geochemical indicators.

Binary spatial random field reconstruction from non-Gaussian inhomogeneous time-series observations

We develop a new model for spatial random field reconstruction of a binary-valued spatial phenomenon. In our model, sensors are deployed in a wireless sensor network across a large geographical region. Each sensor measures a non-Gaussian inhomogeneous temporal process which depends on the spatial phenomenon. Two types of sensors are employed: one collects point observations at specific time points, while the other collects integral observations over time intervals. Subsequently, the sensors transmit these time-series observations to a Fusion Center (FC), and the FC infers the spatial phenomenon from these observations. We show that the resulting posterior predictive distribution is intractable and develop a tractable two-step procedure to perform inference. Firstly, we develop algorithms to perform approximate Likelihood Ratio Tests on the time-series observations, compressing them to a single bit for both point sensors and integral sensors. Secondly, once the compressed observations are transmitted to the FC, we utilize a Spatial Best Linear Unbiased Estimator (S-BLUE) to reconstruct the binary spatial random field at any desired spatial location. The performance of the proposed approach is studied using simulation. We further illustrate the effectiveness of our method using a weather dataset from the National Environment Agency (NEA) of Singapore with fields including temperature and relative humidity.