Indicator Values Research Articles

Performance evaluation of deep learning algorithms for heat loss damage classification in buildings from UAV-borne infrared images

Deep learning (DL) algorithms are gaining popularity to automate anomaly detection in a variety of civil and mechanical engineering domains. Nevertheless, very few attempts have been made to use infrared (IR) images with DL algorithms to detect heat loss in a building and expedite the otherwise cumbersome energy audit process. However, no clear guidelines exist for the selection of appropriate algorithms or hyperparameters (e.g., number of epochs, learning rate, number of images) to implement DL with IR images. To address the research gap, a comparative sensitivity analysis of four well-known DL algorithms for the classification of heat losses using only IR images was performed for the first time. Four DL algorithms, namely, general convolutional neural network (CNN), VGG16, transfer learning (TL) VGG16, and TL inceptionV3 were selected and used for automating the detection of heat loss from IR images. The sensitivity of the selected CNN algorithms was analyzed based on i) the size of the IR images dataset, ii) the number of epochs, and iii) the effect of an unbalanced dataset. The goal was to provide a framework that may aid in the selection of suitable CNN architecture as well as parameters related to data size and an optimal number of training iterations for reliable heat loss identification using IR images. This article has provided a framework for automated heat loss detection that can be applied to any IR dataset without the need to pre-process the IR images. The accuracy of the selected DL algorithms is validated with IR images of laboratory setups and real-world buildings characterized by different types of defects. The comparative sensitivity analysis was conducted based on multiple performance indicators such as precision, recall, and F-1 score which resulted in optimal values of hyperparameters and the most suitable DL algorithm. The automated identification of heat loss yielded performance indicator values of more than 90%.

Studies on physico-chemical parameters in different soil samples from Erode District, Tamil Nadu, India

Soil physicochemical properties can be regarded as an important tool to assess soil health, which further form a base for biological activity in soil. These soil physicochemical properties are comparable in identical land-uses. However, the changes in land-use types and their effects on soil physicochemical properties are largely debates and rather unclear. Soil serves as an important thing in agriculture because this determines the plant growth and health. The present circumstance of industrialization and urbanization has taken a toll on environment, polluting the soil, air and water. Pollution as well as global warming has exhibited detrimental effects on our natural resources of soil becomes the inexplicable. Failing monsoon rains due to climatic changes, population explosion, and depletion of natural water and soil resources such as ground soil, agricultural soil as well as intentional man-made pollution of agriculture pesticide to soil scarcity. Even the scarcely available water is located by contaminants and pollutants which include mutagens, carcinogens, and pathogenic microbes affecting all the file forms affecting the ecological balance. Considering the above mentioned problem criteria, a part of the present work has been designed to analyze the quality of ground soil around the industrial premises. For this objective, we have collected different types of soil samples from various locations in and around sathyamangalam area in Erode District focusing on quite far away from industrial premises considering the concepts of leaching. Basic physio-chemical properties of soil colour, pH, chloride, sulphate, phosphate, silicate, nitrate, moisture, nitrogen, phosphorous, potassium using specific techniques used in laboratory for the samples have been analyzed by APHA standards. These analysis help in the addition of the nutrients of the soil and provide a nutritive indicator value of the sampling sites. Highest possibility of soil quality issues which affects the day to day consumption of soil samples for the domestic needs of the people has been emphasized.

Data mining from process monitoring of typical polluting enterprise.

With the increasing volume of environmental monitoring data, extracting valuable insights from multivariate time series sensor data can facilitate comprehensive information utilization and support informed decision-making in environmental management. However, there is a dearth of comprehensive research on multivariate data analysis for process monitoring in typical polluting enterprises. In this study, an artificial neural network model based on back-propagation algorithm (BP-ANN) was developed to predict the wastewater and exhaust gas emissions using IoT data obtained from process monitoring of a typical polluting enterprise located in Taizhou, Zhejiang Province, China. The results indicate that the model constructed has a high predictive coefficient of determination (R2) with values of 0.8510, 0.9565, 0.9561, 0.9677, and 0.9061 for chemical oxygen demand (COD), potential of hydrogen (pH), electrical conductivity (EC), flue gas emission (FGE), and non-methane hydrocarbon concentration (NMHC) respectively. For the first time, the variable importance measure (VIM)-assisted BP-ANN was employed to investigate the internal and external correlations between wastewater and exhaust gas treatment, thereby enhancing the interpretability of mapping features in the BP-ANN model. The predicted errors for pH and FGE have been demonstrated to fall within the range of - 0.62 ~ 0.30 and - 0.21 ~ 0.15 m3/s, respectively, with average relative errors of 1.05% and 9.60%, which is advantageous in detecting anomalous data and forecasting pollution indicator values. Our approach successfully addresses the challenge of segregating data analysis for wastewater disposal and exhaust gas disposal in the process monitoring of polluting enterprises, while also unearthing potential variables that significantly contribute to the BP-ANN model, thereby facilitating the selection and extraction of characteristic variables.

Climate Change and Wetland Ecosystems: The Effects on Halophilous Vegetation Diversity in Il-Ballut ta’ Marsaxlokk Natura 2000 Site (Malta)

Climate change poses a fundamental threat to the wetlands. The Mediterranean basin is a biodiversity hotspot, and wetlands are important for maintaining this status. The current study evaluated the halophilous vegetation diversity of one of the most relevant Maltese wetlands, Il-Ballut ta’ Marsaxlokk Natura 2000 site, also identified under the Water Framework Directive. A vegetation analysis was carried out according to the Braun–Blanquet approach. The processed dataset included both data from the literature and unpublished data. To quantify vegetation structure and diversity, a hierarchical classification (Chord distance; Ward linkage) and diversity and ecological indices were performed. Diachronic analysis of the taxonomic diversity indices and the Ellenberg indicator values were taken into account. We used an NMDS analysis to assess the ecological fingerprint of the vegetation. In addition, we provided an actual vegetation map for Il-Ballut ta’ Marsaxlokk, based on drone orthophotos. We identified five EU Directive habitats in the study area (1150*, 1310, 1410, 1420, and 92D0) of which one (1150*) was reported for the first time. The ecological fingerprint of the halophilous vegetation has undergone changes over time, particularly due to increasing temperatures. In fact, the results showed that nutrients and temperature were the strongest environmental drivers of the site. The results and methodology of this study demonstrate how vegetation studies can serve as tools to improve knowledge, management actions, and landscape planning of Natura 2000 sites.

Spatial Distribution of Al, Zn, Fe, As, Pb, Mn, Cr, and Cu in Surface Waters of the Urumqi River Basin, China, and Assessment of Risks to Ecosystems and Human Health

The study of pollution and risk assessment of surface water in watersheds is important for the use and management of surface water, as well as for the stability of ecosystems and human health. This study focused on a typical watershed in an arid zone, the Urumqi River basin; divided the basin into upper, middle, and lower reaches according to the main uses of the surface water in the region; and collected surface water samples from the basin. We collected 41 surface water samples from the upper, middle, and lower reaches of the Urumqi River Basin, a typical arid zone watershed. The characteristics and spatial distribution of metal elements in the surface waters of the basin were analysed, the pollution status was evaluated, and risk assessments of the effects of these metal elements on natural ecosystems and human health were carried out. The results showed that (1) the average concentration of the metal element Al in the surface water of the Urumqi River Basin was 663.73 µg·L−1, which was 3.3 times that of the standard limit value (200 µg·L−1), with an exceedance rate of 100%, and the standard deviation value was 136.05 µg·L−1, with a large difference in spatial distribution. Spatial distributions for Al, Cu, Cr, Fe, Mn, and Zn were higher upriver and midstream than downriver, and for Pb and As, they were higher upriver, midstream, and downriver than downriver. (2) The values of the single-factor pollution index of the metal elements Zn, As, Pb, Mn, Cr, and Cu in the upper, middle, and lower reaches of the watershed were all less than 1, which is within the safe range. The integrated pollution indexes of 0.03~0.27 were all less than 0.7, which is within the safe range, and the integrated pollution of the upper reaches was significantly greater than that of the middle and lower reaches. (3) The total ecological risk of the basin ranged from 0.09 to 13.72, which is much lower than the low-risk indicator value of 150, and the ecological risk of the upper reaches was higher than that of the middle and lower reaches. (4) The health risk assessment showed that the total health risks of the eight metal elements to adults and children showed an upstream > downstream > midstream pattern, all of which exceeded the ICRP recommended value (10−5). The average annual total health risks of the carcinogenic metal elements chromium and arsenic to adults and children were 10−6 and 10−5 a−1, respectively, with arsenic concentrations exceeding the ICRP recommended value (10−5). Arsenic and lead in the surface waters of the Urumqi River Basin are important indicators of health risk and need to be prioritised as indicators for environmental risk management.

Socioeconomic inequities of COVID-19 mortality in vulnerable Comunas of the City of Buenos Aires

During the COVID-19 pandemic, the gap in health inequities was exposed and increased, showing how different vulnerable groups were affected. Our aim was to examine the correlation between an area-based health inequity index and mortality due to COVID-19 in people 60 years old or above in the City of Buenos Aires in 2020. We developed a Health Inequity Composite Index (HICI), including six core indicators. Each indicator value per Comuna was first standardized to a Z-score. All six Z-scores were summed into a final composite Z-score to rank the Comunas from lowest to highest social inequities. Comunas from the northern part of the city had lower inequities whereas those in the south had higher levels of inequities. COVID-19 age-standardized mortality rate in people 60 years or above was higher in the Comunas from the south and lower in those from the north. Finally, we found a strong positive correlation (Rho = 0.83, p < 0.0001 CI95% = 0.65–0.99) between HICI and age-standardized mortality rates from COVID-19 in people 60 years or above. Our finding of a strong correlation between the levels of health inequity and mortality calls for a concerted effort in narrowing or eliminating existing inequities.

A pedo‐climatic approach towards soil health assessment: Soil texture‐specific scoring curves on arable land in Norway

AbstractSoil health assessments that integrate physical, chemical and biological indicators help the evaluation of soil functioning, provide a framework for monitoring soil degradation, guide land management activities and secure the delivery of soil ecosystem services. In this study, we assessed soil health by soil texture class on arable land in Southeast Norway and mid‐Norway and between grassland and arable land in mid‐Norway. We used descriptive statistics and the Welch t‐test with unequal variance and Bonferroni corrections to compare a physical soil indicator (bulk density) and chemical indicators (organic matter, P‐AL, K‐AL, Ca‐AL, Mg‐AL, Na‐AL and pH). We developed scoring curves from cumulative normal distribution functions on regional soil data for various soil indicators where climate, soil texture class and land use were considered. Our results show that for certain soil texture classes, average soil indicator values differed between pedo‐climatic zones on arable land, but for others the difference was not significant. The variability between the pedo‐climatic zones for these can be neglected, but for the ones that differ, the variability is important to consider when assessing soil health. Similarly, this was the case when comparing land use (grassland and arable land) for most soil indicators in mid‐Norway. This finding illustrates the importance of addressing unique local conditions in soil health assessments. We propose aggregating similar soil texture classes where no differences are apparent when developing scoring curves. The sub‐optimal levels of plant available nutrients (P‐AL and K‐AL) found in the soil in both pedo‐climatic zones highlights the importance of suitable threshold values for targeted soil ecosystem services to ensure soil health and sustainable agricultural production. We also recommend prioritizing the most relevant soil ecosystem services to limit the number of soil indicators that need monitoring.

Grassland management effect on ecosystem services in the livestock system in an oxisol from the Eastern high plains of Colombia

Colombia has a livestock population of approximately 28.2 million heads, of which 20.4% is found in the eastern high plains of the Orinoquia region. The extensive beef cattle system predominates, whose diet is based on native and introduced pastures of the genus Urochloa sp. which is deficiently managed, affecting productive and reproductive indexes due to the low adoption of technology. We evaluated potential sustainable intensive systems for cattle production, which contribute to maintaining the provision of environmental services. Between 2011 and 2015 in Agrosavia’s Research Center at Carimagua (4° 37'N and 71° 19' W) environmental and animal production variables were monitored in the following systems: a) degraded pastures recovered with tillage and fertilization, b) annual crop rotation with pastures, c) forest arrangements in strips, and d) forest remnants in perimeter areas. Productive and reproductive variables were determined in animals such as weight gain, calving interval, among others while the pasture/crop productive variables included yield and forage quality. Regarding soil ecosystem services (ES) the macrofauna biodiversity, biogeochemical cycles, soil physical and chemical variables were considered. Estimation of indicators was carried out through principal components analysis for soil physical, chemical and macrofauna variables to extract the two main components that explain the variance. For climate regulation of ES, measurement of soil organic carbon (SOC) storage at a depth of 20 cm and the annual accumulated greenhouse gas (GHG) emissions were included. The systems that involved lime amendments and fertilizers increased the value in the year of application. Values in the water regulation indicator did not show significant differences among the options implemented during the years. Edaphic macrofauna biodiversity indicator value was sensitive to changes in management practices, with termites being the group with highest abundance. The indicator related to SOC was higher in the forest area compared to the pasture-crop system. Systems under the agroforestry schemes which integrated various practices such as managed and recovered pastures, crop-pasture rotation, patched areas and tree strips contributed to maintain or improve the ES, although each one of the strategies proposed provided improvement in at leas one ES.

Impact energy assessment of sandwich composites using an ensemble approach boosted by deep learning and electromechanical impedance

Sandwich composites are prone to delamination and fracture during service when exposed to external low-velocity impact. One hindrance to overcome before a broader deployment of sandwich composites is the issue of impact energy assessment (IEA). To promote the solution to this issue, an ensemble deep learning approach is proposed in this study. The approach comprises data expansion, series-to-image conversion, and convolutional neural networks (CNN). The data expansion is implemented using vertical average interpolation. The enhanced data are transformed into images via the Gramian angular summation field to build an image dataset for the CNN model. To validate the developed ensemble approach, hammer-dropping impact experiments on the honeycomb sandwich composites are carried out based on the piezoelectric wafer active sensor network and electromechanical impedance measurement. Accuracy, precision, recall, and F1-score indicators are introduced to evaluate the ensemble approach performance. The above indicator values are all above 0.9600, demonstrating the effectiveness of the proposed ensemble approach in settling the IEA issue.

Genetic Approach to Improve Cryptographic Properties of Balanced Boolean Functions Using Bent Functions

Recently, balanced Boolean functions with an even number n of variables achieving very good autocorrelation properties have been obtained for 12≤n≤26. These functions attain the maximum absolute value in the autocorrelation spectra (without considering the zero point) less than 2n2 and are found by using a heuristic search algorithm that is based on the design method of an infinite class of such functions for a higher number of variables. Here, we consider balanced Boolean functions that are closest to the bent functions in terms of the Hamming distance and perform a genetic algorithm efficiently aiming to optimize their cryptographic properties, which provides better absolute indicator values for all of those values of n for the first time. We also observe that among our results, the functions for 16≤n≤26 have nonlinearity greater than 2n−1−2n2. In the process, our search strategy produces balanced Boolean functions with the best-known nonlinearity for 8≤n≤16.

Implementation of Hybrid RNN-ANFIS on Forecasting Jakarta Islamic Index

RNN is a type of artificial neural network used to handle problems that require sequential data processing. ANFIS is a method that combines the advantages of fuzzy logic and artificial neural networks to create a system, so can adapt the parameters it uses according to the obtained data so that it can build an automated inference system. In this research, we make combination of RNN in ANFIS, which makes ANFIS able to accept input in the form of time series data so that ANFIS can recognize patterns contained in the time series data and its suitable for forecasting cases in the Jakarta Islamic Index. The membership functions used are three Gaussian functions. The results of the RNN-ANFIS Hybrid model training provide an interpretation that the first membership function represents the trend change indicator value, the second membership function represents the closing price change value in the last eight days, and the third membership function represents the pattern change value in the trend. The model for the Jakarta Islamic Index provides very good predictions with an MSE value of 0.001 and an MAE of 0.246.

Trouble in the rice field: Distribution ecology and indicator value of weed species in the rice fields of Himalayan region

Field-based evaluation of indicator weed species in agriculture systems is effective for developing site-specific weed management. Because of anthropogenic activities and climate change, new weeds are invading agricultural fields. The current study examined the weed species in agricultural fields with a focus on diversity, distribution ecological patterns, and identification of indicator species, all of which might help in effective weed management practices. Following the calculation of the Importance Value Index for each weed species, ordination, and indicator species analysis were conducted. We collected vegetation data through random sampling, and the current study recorded 47 species, taxonomically distributed across 41 genera and 19 families. Asteraceae with 13% of species was the leading family followed by Poaceae, and Brassicaceae (11% each). Annual weed species with 49% were the dominant life span category. The phytogeographical analysis revealed that the highest number (28 species) recorded from the study area were native, while 19 species were alien. Of the total alien species, 4 were invasive, and 14 were naturalised and one species Bassia scoparia was casual. The diversity indices show significant differences (p ≤ 0.05) in weeds in rice fields recorded from the study area. Detrended correspondence analysis grouped the species Geranium rotundifolium, Sisymbrium loeselii, Mentha longifolia, Rumex dentatus, Amaranthus viridis on the extreme left side suggest a low score on axis 1 and a high score on axis 2. The analysis of indicator species revealed a significant difference (p ≤ 0.05) in agriculture fields across the sampled sites. According to the indicator species analysis, Poa annua, Persicaria hydropiper, Ranunculus sceleratus, and Echinochloa colona were the key weed species found in the study area. This investigation might aid weed management strategies to minimize crop losses and increase of rice production to fulfill the current food requirement.

Spatial Analysis of Dense LiDAR Point Clouds for Tree Species Group Classification Using Individual Tree Metrics

This study presents a method of tree species classification using individual tree metrics derived from a three-dimensional point cloud from unmanned aerial vehicle laser scanning (ULS). In this novel approach, we evaluated the metrics of 1045 trees using generalized linear model (GLM) and random forest (RF) techniques to automatically assign individual trees into either a coniferous or broadleaf group. We evaluated several statistical descriptors, including a novel approach using the Clark–Evans spatial aggregation index (CE), which indicates the level of clustering in point clouds. A comparison of classifiers that included and excluded the CE indicator values demonstrated their importance for improved classification of the individual tree point clouds. The overall accuracy when including the CE index was 94.8% using a GLM approach and 95.1% using an RF approach. With the RF approach, the inclusion of CE yielded a significant improvement in overall classification accuracy, and for the GLM approach, the CE index was always selected as a significant variable for correct tree class prediction. Compared to other studies, the above-mentioned accuracies prove the benefits of CE for tree species classification, as do the worse results of excluding the CE, where the derived GLM achieved an accuracy of 92.6% and RF an accuracy of 93.8%.

Relative impact of environmental variables on the lake trophic state highlights the complexity of eutrophication controls

For the effective management of lakes apart from defining and monitoring their current state it is crucial to identify environmental variables that are mostly responsible for the nutrient input. We used interpretative machine learning to investigate the environmental parameters that influence the lake's trophic state and recognize their patterns. We analysed the influence of the 25 environmental variables on the commonly used trophic state indicators values: total phosphorus (TP), Chlorophyll-a (Chl-a) and Secchi depth (SD) of 60 lakes located in the Central European Lowlands. We attempted to delineate the lakes into groups due to the influence of common prevailing environment variable/variables on the water trophic state reflected by each indicator. The results indicated that the relative impact of environmental variables on the lake trophic state has an individual hierarchy unique for each indicator. The most important are variables related to catchment impact on the lake, Ohle ratio (L. catchment area/L. area) for TP and Schindler ratio (L. area + L. catchment area)/L. volume for Chl-a and SD. There are also few variables strongly influential only for small sub-groups of lakes that stand out: lake maximum depth, catchment slope steepness expressed by the height standard deviation. The methods used in the study enabled the assessment of the character of the influence of the environmental variables on the indicator value and revealed that most essential variables (Ohle ratio for TP and Schindler ratio for Chl-a and SD) have bimodal distribution with a clear threshold value. These findings contribute to a better understanding of the drivers shaping the lake trophic status and have implication for planning effective management strategies.

BAURAN PROMOSI DAYA TARIK WISATA CURUG SEWU, KENDAL

Curug Sewu waterfall is the highest waterfall in Central Java province, Curug Sewu waterfall is a leading tourist attraction owned by Kendal Regency, besides having natural beauty and very cool air, Curug Sewu waterfall has artificial infrastructure such as a zoo, pond swimming pools, and children's playgrounds, so that the Curug Sewu waterfall has enormous tourism potential, in addition to having tourism potential, tourist attractions must have the right tourism strategy to attract tourists to visit, referring to this, researchers are interested in researching about " Promotion Mix of Curug Sewu Tourism Attraction, Kendal Regency (Case Study of Kendal Regency Disporapar Work Program)”. Data collection techniques in this study include observation, in-depth interviews, documentation, literature study and questionnaires. The technique of determining informants in this study uses purposive sampling which is a technique of determining informants and sources with certain objectives in accordance with the research theme. The results obtained in this study are Curug Sewu has enormous tourism potential and can be further developed, the promotional mix carried out by the Kendal Regency Disporapar is very good, with the mess selling indicator value getting the best results from the four promotional mix indicators that have been carried out by the Kendal Regency Disporapar.

Indirect Prediction of Salt Affected Soil Indicator Properties through Habitat Types of a Natural Saline Grassland Using Unmanned Aerial Vehicle Imagery

Salt meadows, protected within National Parks, cannot be directly surveyed, yet understanding their soil condition is crucial. Our study indirectly estimates soil parameters (Total Salt Content (TSC), Na, and pH) related to salinization/sodification/alkalinization using spectral indices and UAV survey-derived elevation model, focusing on continental lowland salt meadows. A vegetation map was created using 16 spectral indices and a Digital Elevation Model calculated from RGB orthophotos using photogrammetry. Field observations helped define habitat types based on the General National Habitat Classification System (Hungary), and quadrats with complete coverage of specific plant species were identified. Machine learning was employed on 84 training quadrats to develop a prediction algorithm for vegetation patterns. Five saline habitat types, representing variations in soil properties and topography, were identified. Spectral and topomorphometric indices derived from UAV were key to the spatial prediction of soil properties, employing random forest and co-kriging methods. TSC, Na, and pH data served as indicators of salt-affected soils (SAS), and thematic maps were generated for each indicator (57 samples). Overlapping with the vegetation map, the probability range of estimated SAS indicator values was determined. Consequently, a model-based estimation of soil pH, TSC, and Na conditions is provided for habitat types without disturbing protected areas.

Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing

BackgroundVegetation spectral reflectance obtained with hyperspectral imaging (HSI) offer non-invasive means for the non-destructive study of their physiological status. The light intensity at visible and near-infrared wavelengths (VNIR, 0.4–1.0µm) captured by the sensor are composed of mixtures of spectral components that include the vegetation reflectance, atmospheric attenuation, top-of-atmosphere solar irradiance, and sensor artifacts. Common methods for the extraction of spectral reflectance from the at-sensor spectral radiance offer a trade-off between explicit knowledge of atmospheric conditions and concentrations, computational efficiency, and prediction accuracy, and are generally geared towards nadir pointing platforms. Therefore, a method is needed for the accurate extraction of vegetation reflectance from spectral radiance captured by ground-based remote sensors with a side-facing orientation towards the target, and a lack of knowledge of the atmospheric parameters.ResultsWe propose a framework for obtaining the vegetation spectral reflectance from at-sensor spectral radiance, which relies on a time-dependent Encoder-Decoder Convolutional Neural Network trained and tested using simulated spectra generated from radiative transfer modeling. Simulated at-sensor spectral radiance are produced from combining 1440 unique simulated solar angles and atmospheric absorption profiles, and 1000 different spectral reflectance curves of vegetation with various health indicator values, together with sensor artifacts. Creating an ensemble of 10 models, each trained and tested on a separate 10% of the dataset, results in the prediction of the vegetation spectral reflectance with a testing r2 of 98.1% (±0.4). This method produces consistently high performance with accuracies >90% for spectra with resolutions as low as 40 channels in VNIR each with 40 nm full width at half maximum (FWHM) and greater, and remains viable with accuracies >80% down to a resolution of 10 channels with 60 nm FWHM. When applied to real sensor obtained spectral radiance data, the predicted spectral reflectance curves showed general agreement and consistency with those corrected by the Compound Ratio method.ConclusionsWe propose a method that allows for the accurate estimation of the vegetation spectral reflectance from ground-based HSI platforms with sufficient spectral resolution. It is capable of extracting the vegetation spectral reflectance at high accuracy in the absence of knowledge of the exact atmospheric compositions and conditions at time of capture, and the lack of available sensor-measured spectral radiance and their true ground-truth spectral reflectance profiles.

Classification of forest vegetation in Yaraligöz Education and Observation Forest, Kastamonu, Türkiye

Abstract. Karaköse M, Terzioglu S. 2023. Classification of forest vegetation in Yaraligöz Education and Observation Forest, Kastamonu, Türkiye. Nusantara Bioscience 15: 161-171. Forest ecosystems, which contain approximately half of the 12,975 plant taxa known to be distributed in Türkiye, are very important in terms of biological diversity. The fact that forests are seen as wood-producing ecosystems and have been planned for these purposes for many years has resulted in forests being kept away from being evaluated in terms of ecosystem and socio-economic aspects. Today, forest management plans have begun to be prepared in which all components of the forest are considered, and biological diversity is reflected. Based on this understanding, this investigation was performed during 2011-2012 to analyze the forest vegetation of Yaraligöz Education and Observation Forest. The study area is located in the north-western part of Türkiye within the Euro-Siberian phytogeographic region. Phytosociological studies were carried out using the classical Braun-Blanquet approach, and 51 relevés were collected from the research area. The database contains 184 vascular plant taxa, dominated by the Euro-Siberian elements and Hemicryptophytes. The relevés were classified using the Modified TWINSPAN, and the forest vegetation types' distribution was analyzed using non-metric multi-dimensional scaling. A comprehensive comparison among all described syntaxa within the study area and other closely related forests was provided. Topographic factors and mean Ellenberg indicator values were evaluated to interpret the ecological relationships among forest plant associations. Four associations, two (Daphno pontica-Pinetum hamatae and Galio rotundifolii-Abietum equi-trojani) of which are new syntaxa to science, were defined as belonging to the Euxine Province within Erico-Pinetea, Carpino-Fagetea sylvaticae, Quercetea pubescentis, and Vaccinio-Piceetea. The distribution of these four forest associations was mostly affected by light intensity, nutrients, moisture content, and altitude. The results of this vegetation study provided precious knowledge for monitoring vegetation change and nature conservation projects in the Yaraligöz.

Can Moisture-Indicating Understory Plants Be Used to Predict Survivorship of Large Lodgepole Pine Trees During Severe Outbreaks of Mountain Pine Beetle?

Abstract Why do some mature lodgepole pines survive mountain pine beetle outbreaks while most are killed? Here we test the hypothesis that mature trees growing in sites with vascular plant indicators of high relative soil moisture are more likely to survive mountain pine beetle outbreaks than mature trees associated with indicators of lower relative soil moisture. Working in the Clearwater Valley of south central British Columbia, we inventoried understory plants growing near large-diameter and small-diameter survivors and nonsurvivors of a mountain pine beetle outbreak in the mid-2000s. When key understory species were ranked according to their accepted soil moisture indicator value, a significant positive correlation was found between survivorship in large-diameter pine and inferred relative high soil moisture status—a finding consistent with the well-documented importance of soil moisture in the mobilization of defense compounds in lodgepole pine. We suggest that indicators of soil moisture may be useful in predicting the survival of large pine trees in future pine beetle outbreaks. Study Implications: A recent outbreak of the mountain pine beetle resulted in unprecedented levels of lodgepole pine mortality across southern inland British Columbia. Here, we use moisture-dependent understory plants to show that large lodgepole pine trees growing in sites with high relative moisture are more likely than similar trees in drier sites to survive severe outbreaks of mountain pine beetle—a finding that may be related to a superior ability to mobilize chemical defense compounds compared with drought-stressed trees.

Dry grasslands adjacent to organic fields have higher plant diversity − Even far into their interior

Dry semi-natural grasslands are highly species-rich, and therefore often protected in cultural landscapes of Central Europe. However, reserves are typically small and may be subjected to edge effects. Here, we ask how grassland biodiversity changes from edges neighbouring arable fields into grassland interior. We compared plant diversity of protected grasslands adjacent to organically versus conventionally managed arable fields. We surveyed the vegetation along 50 m transects from the arable field border into the grasslands, and compared site characteristics via Ellenberg indicator values and measured soil parameters. Averaged linear mixed-effects models showed an increase of diversity with increasing distance from the adjacent field, from a mean observed richness of 25 species per 4 m²-plot at the border to more than 30 species in the interior. For organic sites, models consistently showed 13 species more per plot than for conventional sites. Diversity for the two land use types remained different over the entire transect length. Thus, the effect of adjacent field farming extends far into the interior of the protected grasslands, yet overall organic farming is less detrimental. We assume that this is related to a combination of differences in local farm management, such as pesticide application, and the wider landscape context associated with the farm type. Buffer zones with at least organic-like management could improve the conservation value of nature reserves.