FSC-certified forest management benefits large mammals compared to non-FSC.

More than a quarter of the world's tropical forests are exploited for timber1. Logging impacts biodiversity in these ecosystems, primarily through the creation of forest roads that facilitate hunting for wildlife over extensive areas. Forest management certification schemes such as the Forest Stewardship Council (FSC) are expected to mitigate impacts on biodiversity, but so far very little is known about the effectiveness of FSC certification because of research design challenges, predominantly limited sample sizes2,3. Here we provide this evidence by using 1.3 million camera-trap photos of 55 mammal species in 14 logging concessions in western equatorial Africa. We observed higher mammal encounter rates in FSC-certified than in non-FSC logging concessions. The effect was most pronounced for species weighing more than 10 kg and for species of high conservation priority such as the critically endangered forest elephant and western lowland gorilla. Across the whole mammal community, non-FSC concessions contained proportionally more rodents and other small species than did FSC-certified concessions. The first priority for species protection should be to maintain unlogged forests with effective law enforcement, but for logged forests our findings provide convincing data that FSC-certified forest management is less damaging to the mammal community than is non-FSC forest management. This study provides strong evidence that FSC-certified forest management or equivalently stringent requirements and controlling mechanisms should become the norm for timber extraction to avoid half-empty forests dominated by rodents and other small species.

Occupational accidents in Slovak Military Forests and Estates: incidence, timing, and trends over 10 years.

Introduction: Forestry provides a wide range of employment opportunities worldwide and is seen as one of the high-risk industries in terms of occupational accidents. Objectives: The submitted study analyzed the injury rate in the Military Forests and Estates of the Slovak Republic (62.6 thousand ha) between 2013 and 2022. Methods: The data analyses included regression and correlation analyses, χ2 tests to analyze the relationships between studied variables, and incidence rates. Results: During the observed period, employees suffered 26 occupational accidents, of which 19.2% were light, 57.7% were registered, 23.1% were severe, and 0% were fatal. For every 1 million m3 of harvested timber, 7.7 accidents occurred. The incidence rate during the observed period was 672.1/100,000 employees. The highest proportion of accidents was in the age group 51-60 years and in employees with the lowest length of work experience <5 years. Regarding time, the highest proportion of occupational accidents occurred between 8:01 and 10:00 AM (53.8%) and day-wise on Thursdays (46.2%). The highest proportion of accidents occurred among forest workers (65.3%) during pruning and silviculture activities (42.3%). The most common injury site was forest stands (65.3%). Superficial injuries (34.6%) were the most common, mainly affecting the lower limbs (50%). The most frequent material agents causing the accidents were work and transport areas as sources of worker fall (38.5%), and the most frequent reason for an accident to occur was the lack of personal requirements for proper work performance (92.4%), whereas only (3.8%) of accidents occurred due to the use of forbidden or hazardous working procedures. Conclusion: The presented study identified the most vulnerable worker groups and provided an overview of the overall injury rate at the state forest company in Slovakia. The documentation can be incorporated into the safety strategies of forest enterprises.

A dataset for fine-grained seed recognition.

The research of plant seeds has always been a focus of agricultural and forestry research, and seed identification is an indispensable part of it. With the continuous application of artificial intelligence technology in the field of agriculture, seed identification through computer vision can effectively promote the development of agricultural and forestry wisdom. Data is the foundation of computer vision, but there is a lack of suitable datasets in the agricultural field. In this paper, a seed dataset named LZUPSD is established. A device based on mobile phones and macro lenses was established to acquire images. The dataset contains 4496 images of 88 different seeds. This dataset can not only be used as data for training deep learning models in the computer field, but also provide important data support for agricultural and forestry research. As an important resource in this field, this dataset plays a positive role in modernizing agriculture and forestry.

Forestry trials and species adaptability to climate change.

Most climate change analyses of extinction risk rely on species' climatic requirements determined from only their natural distributions. Many tree species can grow successfully under climatic conditions distinctly different from those of their natural distributions. Gathering together results from these introduction trials would help to assess the importance of this climatic adaptability for different tree species.

Vision System for a Forestry Navigation Machine.

This article presents the development of a vision system designed to enhance the autonomous navigation capabilities of robots in complex forest environments. Leveraging RGBD and thermic cameras, specifically the Intel RealSense 435i and FLIR ADK, the system integrates diverse visual sensors with advanced image processing algorithms. This integration enables robots to make real-time decisions, recognize obstacles, and dynamically adjust their trajectories during operation. The article focuses on the architectural aspects of the system, emphasizing the role of sensors and the formulation of algorithms crucial for ensuring safety during robot navigation in challenging forest terrains. Additionally, the article discusses the training of two datasets specifically tailored to forest environments, aiming to evaluate their impact on autonomous navigation. Tests conducted in real forest conditions affirm the effectiveness of the developed vision system. The results underscore the system's pivotal contribution to the autonomous navigation of robots in forest environments.

Bornean tropical forests recovering from logging at risk of regeneration failure.

Active restoration through silvicultural treatments (enrichment planting, cutting climbers and liberation thinning) is considered an important intervention in logged forests. However, its ability to enhance regeneration is key for long-term recovery of logged forests, which remains poorly understood, particularly for the production and survival of seedlings in subsequent generations. To understand the long-term impacts of logging and restoration we tracked the diversity, survival and traits of seedlings that germinated immediately after a mast fruiting in North Borneo in unlogged and logged forests 30-35 years after logging. We monitored 5119 seedlings from germination for ~1.5 years across a mixed landscape of unlogged forests (ULs), naturally regenerating logged forests (NR) and actively restored logged forests via rehabilitative silvicultural treatments (AR), 15-27 years after restoration. We measured 14 leaf, root and biomass allocation traits on 399 seedlings from 15 species. Soon after fruiting, UL and AR forests had higher seedling densities than NR forest, but survival was the lowest in AR forests in the first 6 months. Community composition differed among forest types; AR and NR forests had lower species richness and lower evenness than UL forests by 5-6 months post-mast but did not differ between them. Differences in community composition altered community-weighted mean trait values across forest types, with higher root biomass allocation in NR relative to UL forest. Traits influenced mortality ~3 months post-mast, with more acquisitive traits and relative aboveground investment favoured in AR forests relative to UL forests. Our findings of reduced seedling survival and diversity suggest long time lags in post-logging recruitment, particularly for some taxa. Active restoration of logged forests recovers initial seedling production, but elevated mortality in AR forests lowers the efficacy of active restoration to enhance recruitment or diversity of seedling communities. This suggests current active restoration practices may fail to overcome barriers to regeneration in logged forests, which may drive long-term changes in future forest plant communities.

A restauração ativa por meio de tratamentos silviculturais (plantio de enriquecimento, corte de trepadeiras e desbaste) é considerada uma intervenção importante em florestas com exploração de madeira. No entanto, sua capacidade de melhorar a regeneração, essencial para a recuperação de longo prazo das florestas exploradas, permanece pouco compreendida, especialmente no que diz respeito à produção e sobrevivência de mudas em gerações subsequentes. Para compreender os impactos de longo prazo da exploração madeireira e da restauração, acompanhamos a diversidade, sobrevivência e características de plântulas que germinaram imediatamente após uma frutificação em massa no norte de Bornéu, em florestas com e sem exploração de madeira, 30-35 anos após o fim da extração. Monitoramos 5119 mudas desde a germinação por aproximadamente 1,5 anos em uma paisagem mista de florestas não exploradas (UL), florestas exploradas em regeneração natural (NR) e florestas exploradas restauradas ativamente por meio de tratamentos silviculturais de reabilitação (AR), 15-27 anos após a restauração. Medimos 14 traços funcionais de folhas, raízes e alocação de biomassa em 399 mudas de 15 espécies. Logo após a frutificação, as florestas UL e AR apresentaram densidades de mudas mais altas do que as florestas NR, mas a sobrevivência foi mais baixa nas florestas AR nos primeiros seis meses. A composição da comunidade diferiu entre os tipos de floresta; as florestas AR e NR teviram menor riqueza de espécies e menor equidade do que as florestas UL 5-6 meses após a frutificação, mas não diferiram entre si. As diferenças na composição da comunidade alteraram os valores de média ponderada pela comunidade das características entre os tipos de floresta com maior alocação de biomassa radicular nas florestas NR em relação às florestas UL. As características influenciaram a mortalidade aproximadamente 3 meses após a frutificação, com traços mais aquisitivos maior investimento em biomassa relativa acima do solo nas florestas AR em relação às florestas UL. Nossas descobertas de redução na sobrevivência e diversidade de plântulas sugerem que há longos retardos no recrutamento após o fim da exploração de madeira, particularmente para alguns táxons. A restauração ativa de florestas exploradas recupera a produção inicial de plântulas, mas a mortalidade elevada nas florestas AR diminui a eficácia da restauração ativa no melhorio do recrutamento e da diversidade das comunidades de mudas. Isso sugere que as práticas atuais de restauração ativa podem não superar as barreiras à regeneração em florestas exploradas, o que pode levar a mudanças de longo prazo nas comunidades florestais no futuro.

Surveillance of tick-borne bacteria infection in ticks and forestry populations in Inner Mongolia, China.

Ticks are one of the most important vectors that can transmit pathogens to animals and human beings. This study investigated the dominant tick-borne bacteria carried by ticks and tick-borne infections in forestry populations in Arxan, Inner Mongolia, China. Ticks were collected by flagging from May 2020 to May 2021, and blood samples were collected from individuals at high risk of acquiring tick-borne diseases from March 2022 to August 2023. The pooled DNA samples of ticks were analyzed to reveal the presence of tick-borne bacteria using high-throughput sequencing of the 16S rDNA V3-V4 region, and species-specific polymerase chain reaction (PCR) related to sequencing was performed to confirm the presence of pathogenic bacteria in individual ticks and human blood samples. All sera samples were examined for anti-SFGR using ELISA and anti-B. burgdorferi using IFA and WB. A total of 295 ticks (282 Ixodes persulcatus and 13 Dermacentor silvarum) and 245 human blood samples were collected. Rickettsia, Anaplasma, Borrelia miyamotoi, and Coxiella endosymbiont were identified in I. persulcatus by high-throughput sequencing, while Candidatus R. tarasevichiae (89.00%, 89/100), B. garinii (17.00%, 17/100), B. afzelii (7.00%, 7/100), and B. miyamotoi (7.00%, 7/100) were detected in I. persulcatus, as well the dual co-infection with Candidatus R. tarasevichiae and B. garinii were detected in 13.00% (13/100) of I. persulcatus. Of the 245 individuals, B. garinii (4.90%, 12/245), R. slovaca (0.82%, 2/245), and C. burnetii (0.41%, 1/245) were detected by PCR, and the sequences of the target genes of B. garinii detected in humans were identical to those detected in I. persulcatus. The seroprevalence of anti-SFGR and anti-B. burgdorferi was 5.71% and 13.47%, respectively. This study demonstrated that Candidatus R. tarasevichiae and B. garinii were the dominant tick-borne bacteria in I. persulcatus from Arxan, and that dual co-infection with Candidatus R. tarasevichiae and B. garinii was frequent. This is the first time that B. miyamotoi has been identified in ticks from Arxan and R. solvaca has been detected in humans from Inner Mongolia. More importantly, this study demonstrated the transmission of B. garinii from ticks to humans in Arxan, suggesting that long-term monitoring of tick-borne pathogens in ticks and humans is important for the prevention and control of tick-borne diseases.

Tree parameter extraction in Fokienia hodginsii plantation based on airborne LiDAR data.

Accurate and efficient extraction of tree parameters from plantations lay foundation for estimating individual wood volume and stand stocking. In this study, we proposed a method of extracting high-precision tree parameters based on airborne LiDAR data. The main process included data pre-processing, ground filtering, individual tree segmentation, and parameter extraction. We collected high-density airborne point cloud data from the large-diameter timber of Fokienia hodginsii plantation in Guanzhuang State Forestry Farm, Shaxian County, Fujian Province, and pre-processed the point cloud data by denoising, resampling and normalization. The vegetation point clouds and ground point clouds were separated by the Cloth Simulation Filter (CSF). The former data were interpolated using the Delaunay triangulation mesh method to generate a digital surface model (DSM), while the latter data were interpolated using the Inverse Distance Weighted to generate a digital elevation model (DEM). After that, we obtained the canopy height model (CHM) through the difference operation between the two, and analyzed the CHM with varying resolutions by the watershed algorithm on the accuracy of individual tree segmentation and parameter extraction. We used the point cloud distance clustering algorithm to segment the normalized vegetation point cloud into individual trees, and analyzed the effects of different distance thresholds on the accuracy of indivi-dual tree segmentation and parameter extraction. The results showed that the watershed algorithm for extracting tree height of 0.3 m resolution CHM had highest comprehensive evaluation index of 91.1% for individual tree segmentation and superior accuracy with R2 of 0.967 and RMSE of 0.890 m. When the spacing threshold of the point cloud segmentation algorithm was the average crown diameter, the highest comprehensive evaluation index of 91.3% for individual tree segmentation, the extraction accuracy of the crown diameter was superior, with R2 of 0.937 and RMSE of 0.418 m. Tree height, crown diameter, tree density, and spatial distribution of trees were estimated. There were 5994 F. hodginsii, with an average tree height of 16.63 m and crown diameter of 3.98 m. Trees with height of 15-20 m were the most numerous (a total of 2661), followed by those between 10-15 m. This method of forest parameter extraction was useful for monitoring and managing plantations.

Conflicts and opportunities for commercial tree plantation expansion and biodiversity restoration across Brazil.

Substantial global restoration commitments are occurring alongside a rapid expansion in land-hungry tropical commodities, including to supply increasing demand for wood products. Future commercial tree plantations may deliver high timber yields, shrinking the footprint of production forestry, but there is an as-yet unquantified risk that plantations may expand into priority restoration areas, with marked environmental costs. Focusing on Brazil-a country of exceptional restoration importance and one of the largest tropical timber producers-we use random forest models and information on the economic, social, and spatial drivers of historic commercial tree plantation expansion to estimate and map the probability of future monoculture tree plantation expansion between 2020 and 2030. We then evaluate potential plantation-restoration conflicts and opportunities at national and biome-scales and under different future production and restoration pathways. Our simulations show that of 2.8 Mha of future plantation expansion (equivalent to plantation expansion 2010-2020), ~78,000 ha (3%) is forecast to occur in the top 1% of restoration priority areas for terrestrial vertebrates, with ~547,500 ha (20%) and ~1,300,000 ha (46%) in the top 10% and 30% of priority areas, respectively. Just ~459,000 ha (16%) of expansion is forecast within low-restoration areas (bottom 30% restoration priorities), and the first 1 Mha of plantation expansion is likely to have disproportionate impacts, with potential restoration-plantation overlap starkest in the Atlantic Forest but prominent in the Pampas and Cerrado as well. Our findings suggest that robust, coherent land-use policies must be deployed to ensure that significant trade-offs between restoration and production objectives are navigated, and that commodity expansion does not undermine the most tractable conservation gains under emerging global restoration agendas. They also highlight the potentially significant role an engaged forestry sector could play in improving biodiversity outcomes in restoration projects in Brazil, and presumably elsewhere.

Multi-source information fusion-driven corn yield prediction using the Random Forest from the perspective of Agricultural and Forestry Economic Management.

The objective of this study is to promptly and accurately allocate resources, scientifically guide grain distribution, and enhance the precision of crop yield prediction (CYP), particularly for corn, along with ensuring application stability. The digital camera is selected to capture the digital image of a 60 m × 10 m experimental cornfield. Subsequently, the obtained data on corn yield and statistical growth serve as inputs for the multi-source information fusion (MSIF). The study proposes an MSIF-based CYP Random Forest model by amalgamating the fluctuating corn yield dataset. In relation to the spatial variability of the experimental cornfield, the fitting degree and prediction ability of the proposed MSIF-based CYP Random Forest are analyzed, with statistics collected from 1-hectare, 10-hectare, 20-hectare, 30-hectare, and 50-hectare experimental cornfields. Results indicate that the proposed MSIF-based CYP Random Forest model outperforms control models such as support vector machine (SVM) and Long Short-Term Memory (LSTM), achieving the highest prediction accuracy of 89.30%, surpassing SVM and LSTM by approximately 13.44%. Meanwhile, as the experimental field size increases, the proposed model demonstrates higher prediction accuracy, reaching a maximum of 98.71%. This study is anticipated to offer early warnings of potential factors affecting crop yields and to further advocate for the adoption of MSIF-based CYP. These findings hold significant research implications for personnel involved in Agricultural and Forestry Economic Management within the context of developing agricultural economy.

Proceed with Caution: Social Acceptability of Forestry Practices in Puerto Rico among Members of Local Environmental Organizations, Academia and Professional Associations.

Social acceptability of forestry practices plays a key role in defining sustainable forestry policies and strategies. In this study an online survey was distributed among members of environmental, non-governmental, professional, and academic organizations to assess the acceptability of forestry practices in Puerto Rico among members of civic society interested in environmental management issues. Participants were asked about their perception of forest uses, their preference of tree harvesting technologies, methods that may apply in small scale wood production settings, and trust in organizations providing forest information. We also inquired about attitudes towards economic activity in forests and the impact of such activity on recreation and biodiversity. The results show that even though participants do not place a high priority on economic development through forestry activities, acceptance of forest management for wood harvesting will be possible by considering adherence to particular forestry technologies and methods to safeguard current recreation activities and biodiversity conservation. Social acceptability information would be worthwhile when seeking consensus among a broader group of local stakeholders. As a next step we suggest the creation of a council constituted by diverse forestry sector stakeholders that would engage in a strategic planning exercise to delineate a clear road map that can guide short and long-term sustainable forest management, including wood industry development.

A chromosome-level genome assembly of the forestry pest Coronaproctus castanopsis.

As an important forestry pest, Coronaproctus castanopsis (Monophlebidae) has caused serious damage to the globally valuable Gutianshan ecosystem, China. In this study, we assembled the first chromosome-level genome of the female specimen of C. castanopsis by merging BGI reads, HiFi long reads and Hi-C data. The assembled genome size is 700.81 Mb, with a scaffold N50 size of 273.84 Mb and a contig N50 size of 12.37 Mb. Hi-C scaffolding assigned 98.32% (689.03 Mb) of C. Castanopsis genome to three chromosomes. The BUSCO analysis (n = 1,367) showed a completeness of 91.2%, comprising 89.2% of single-copy BUSCOs and 2.0% of multicopy BUSCOs. The mapping ratio of BGI, second-generation RNA, third-generation RNA and HiFi reads are 97.84%, 96.15%, 97.96%, and 99.33%, respectively. We also identified 64.97% (455.3 Mb) repetitive elements, 1,373 non-coding RNAs and 10,542 protein-coding genes. This study assembled a high-quality genome of C. castanopsis, which accumulated valuable molecular data for scale insects.

Legacy of last millennium timber use on plant cover in Central Europe: Insights from tree rings and pollen.

Throughout history, humans have relied on wood for constructions, tool production or as an energy source. How and to what extent these human activities have impacted plant abundance and composition over a long-term perspective is, however, not well known. To address this knowledge gap, we combined 44,239 precisely dated tree-ring samples from economically and ecologically important tree species (spruce, fir, pine, oak) from historical buildings, and pollen-based plant cover estimates using the REVEALS model from 169 records for a total of 34 1° × 1° grid cells for Central Europe. Building activity and REVEALS estimates were compared for the entire study region (4-15°E, 46-51°N), and for low (<500 m asl) and mid/high elevations (≥500 m asl) in 100-year time windows over the period 1150-1850. Spruce and oak were more widely used in wooden constructions, amounting to 35 % and 32 %, respectively, compared to pine and fir. Besides wood properties and species abundance, tree diameters of harvested individuals, being similar for all four species, were found to be the most crucial criterion for timber selection throughout the last millennium. Regarding land use changes, from the 1150-1250's onwards, forest cover generally decreased due to deforestation until 1850, especially at lower elevations, resulting in a more heterogeneous landscape. The period 1650-1750 marks a distinct change in the environmental history of Central Europe; increasing agriculture and intense forest management practices were introduced to meet the high demands of an increasing population and intensifying industrialization, causing a decrease in palynological diversity, especially at low elevations. Likely the characteristic vegetation structure and composition of contemporary landscapes originated from that period. We further show that land use has impacted vegetation composition and diversity at an increasing speed leading to a general homogenization of landscapes through time, highlighting the limited environmental benefits of even-aged plantation forestry.

Quo vadis, smallholder forest landscape? An introduction to the LPB-RAP model.

The impacts of the Anthropocene on climate and biodiversity pose societal and ecological problems that may only be solved by ecosystem restoration. Local to regional actions are required, which need to consider the prevailing present and future conditions of a certain landscape extent. Modeling approaches can be of help to support management efforts and to provide advice to policy making. We present stage one of the LaForeT-PLUC-BE model (Landscape Forestry in the Tropics-PCRaster Land Use Change-Biogeographic & Economic model; in short: LPB) and its thematic expansion module RAP (Restoration Areas Potentials). LPB-RAP is a high-resolution pixel-based scenario tool that relies on a range of explicit land use types (LUTs) to describe various forest types and the environment. It simulates and analyzes future landscape configurations under consideration of climate, population and land use change long-term. Simulated Land Use Land Cover Change (LULCC) builds on dynamic, probabilistic modeling incorporating climatic and anthropogenic determinants as well as restriction parameters to depict a sub-national regional smallholder-dominated forest landscape. The model delivers results for contrasting scenario settings by simulating without and with potential Forest and Landscape Restoration (FLR) measures. FLR potentials are depicted by up to five RAP-LUTs. The model builds on user-defined scenario inputs, such as the Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). Model application is here exemplified for the SSP2-RCP4.5 scenario in the time frame 2018-2100 on the hectare scale in annual resolution using Esmeraldas province, Ecuador, as a case study area. The LPB-RAP model is a novel, heuristic Spatial Decision Support System (SDSS) tool for smallholder-dominated forest landscapes, supporting near-time top-down planning measures with long-term bottom-up modeling. Its application should be followed up by FLR on-site investigations and stakeholder participation across all involved scales.

Abiotic factors impact on oak forest decline in Lorestan Province, Western Iran.

The Zagros oak forests in Iran are facing a concerning decline due to prolonged and severe drought conditions over several decades, compounded by the simultaneous impact of temperature on oak populations. This study in oak woodlands of central Zagros forests in Lorestan province analyzed abiotic factors such as climate properties, topographic features, land use, and soil properties from 1958 to 2022. We found that higher elevation areas with steeper slopes and diverse topography show significant potential for enhancing oak tree resilience in the face of climate change. Additionally, traditional land use practices like livestock keeping and dryland farming contribute to a widespread decline in oak populations. Preserving forest biodiversity and ensuring ecological sustainability requires immediate attention. Implementing effective land-use management strategies, such as protecting and regulating human-forest interaction, and considering meteorological factors to address this issue is crucial. Collaborative efforts from stakeholders, policymakers, and local communities are essential to oppose destructive suburban sprawl and other developments. Sustainable forestry practices should be implemented to improve the living standards of local communities that rely on forests and traditional livestock keeping, offer forestry-related jobs, and ensure social security. Such efforts are necessary to promote conservation awareness and sustainable practices, safeguarding this unique and vital ecosystem for future generations.

Sensors for Digital Transformation in Smart Forestry.

Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition in the digital transformation of forestry. However, the complexity and challenging conditions of forest environments often impede data collection efforts. Achieving the full potential of smart forestry necessitates a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data essential for AI applications. This paper highlights the symbiotic relationship between human expertise and the digital transformation in forestry, particularly under challenging conditions. We emphasize the human-in-the-loop approach, which allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. A critical aspect of this integration is the deployment of autonomous robotic systems in forests, functioning both as data collectors and processing hubs. These systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. We present our universal sensor platform, detailing our experiences and the critical importance of the initial phase in digital transformation-the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and our findings underscore its significance in advancing smart forestry.

The unique association between serum 25-hydroxyvitamin D concentrations and blood lipid profiles in agriculture, forestry, and fishing occupations: Insights from NHANES 2001-2014.

BACKGROUND: The relationship of serum 25(OH)D levels and hyperlipidemia has not been explored in the Agriculture, Forestry, and Fishing (AFF) occupation. We aimed to explore the impact of serum 25(OH)D levels on lipid profiles in AFF workers, traffic drivers, and miners. METHODS: Data from 3937 adults aged 18-65 years old with completed information were obtained from the National Health and Examination Survey from 2001 to 2014. Multivariate linear regression models were used to examine the associations between serum 25(OH)D concentrations and triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and HDL-C/LDL-C ratio. Subgroup analyses for AFF workers considered age, sex, BMI, work activity, months worked, and alcohol consumption. Non-linear relationships were explored using curve fitting. RESULTS: Serum 25(OH)D levels differed between groups (AFF: 60.0 ± 21.3 nmol/L, drivers: 56.6 ± 22.2 nmol/L, miners: 62.8 ± 22.3 nmol/L). Subgroup analysis of the AFF group showed that participants with serum 25(OH)D ≥50 nmol/L, females, and BMI <30 kg/m2 demonstrated improved HDL-C levels correlating with higher serum 25(OH)D. Serum 25(OH)D in AFF workers had a reversed U-shaped relationship with TG and TC, and a U-shaped relationship with HDL-C, with HDL-C, with inflection points at 49.5 nmol/L for TG and TC, and 32.6 nmol/L for HDL-C. CONCLUSIONS: Serum 25(OH)D levels are associated with lipid profiles, and the relationship varies among occupational groups. AFF workers, facing unique occupational challenges, may benefit from maintaining adequate serum 25(OH)D levels to mitigate adverse lipid profiles and reduce cardiovascular risk.

Historical changes in tree and impervious surface cover following urban renewal in a small postindustrial city.

Changes in tree cover and impervious surfaces have been observed across many cities in the United States over the past 70 years. Many municipalities are implementing tree planting programs in efforts to increase tree cover. A detailed understanding of historical changes in land cover can inform urban forest management. I applied a convolutional neural network image segmentation approach to historical aerial imagery to delineate changes in land cover in 1957, 1974, and 2017 in Utica, New York, a small, postindustrial city. The model predicted tree, pavement, and building land cover in each year with overall accuracies ranging from 82-87%. From 1957 to 2017, tree cover declined in many areas and impervious surface cover (buildings and pavement) increased. Tree cover gains largely occurred in uninhabited, natural areas; whereas, the greatest declines in tree coverage occurred in many residential areas following the start of the urban renewal efforts in 1957. Current tree planting efforts targeted at homeowners could drive disparities in future tree cover since several areas of Utica with low tree have a high proportion of renter occupied homes and a low median household income. Convolutional Neural Network approaches for image segmentation of aerial imagery are a helpful tool in understanding patterns in changes in tree and impervious surfaces. A better understanding of the legacies of historical policies and neighborhood-scale changes in land cover can assist in highlighting priorities for urban forest management and justice-oriented urban forestry approaches to urban tree planting.