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ABSTRACTMaterial extrusion (MEX) 3D printing offers a promising avenue for fabricating metal and ceramic components, where highly loaded polymer filaments are 3D-printed and sintered. Achieving nearly 100% 3D-printed part density is critical in this process, as porosity during printing compromises part properties after sintering. However, challenges arise due to the unfavourable mechanical and rheological properties of MEX metal filaments, leading to slow print-speeds, filament breakage, and inconsistent extrusion. This work explores these process-property correlations by developing process maps for bound metal filaments and unfilled polymer systems. This study throws light on the significance of back-flow and back-pressure in calculating the pressure drop across the nozzle, especially in a diverse set of material systems represented by hard and stiff PLA, soft and flexible TPU, and brittle and fragile bronze filaments. Additionally, ANOVA identified the effect of print conditions on measured variables, while regression models helped predict the behaviour of a given material under different process conditions. Therefore, this study enables material design and discovery for bound-metal filaments while addressing critical knowledge gaps, thereby paving the way for high-density 3D-printed components.

Climate change is one of the main consequences of anthropogenic activities. Since the 1950s, gradual changes and an increase in climate warming have been observed. Previous research has been indicating potential associations between climate warming and spatiotemporal changes in precipitation. Moreover, the regional patterns of precipitation have a key role in the continuous monitoring of climate characteristics and natural hazards such as floods and droughts. Therefore, precise and accurate measurements of precipitation concentration and spatiotemporal variability in their patterns are very crucial. In this study, a new method for measuring precipitation concentration is developed and applied to 54 meteorological stations in Pakistan. Furthermore, to assess the precipitation patterns, the proposed method provides solid evidence for considering the effect of temperatures under climate warming. Furthermore, using the spatial correlation between the proposed method and its competitor, a comparative analysis is made to evaluate the performance of the proposed method. Moreover, the spatial variability structure in various precipitation patterns is assessed and compared using spatial predictive maps. Outcomes associated with this research show significant deviations between the proposed method and the existing one. In this paper, regression analysis revealed that the additional input can potentially improve the precipitation estimates under the appropriate sampling estimator. This is the first study that has documented the impact of climate warming on measuring precipitation concentration. These findings can contribute to a better understanding of precipitation concentration in relation to climate warming. HIGHLIGHTS Develop a new method (RCTIPC) for precipitation modelling.; Comparison between PCI and RCTIPC for precipitation modelling.; Analyzing regional precipitation variability with climate change impact.; Spatial variability structure in various precipitation patterns is assessed and compared using spatial predictive maps.;

Lead (Pb) is one of the most hazardous heavy-metal pollutants in the environment. However, the resistance of different soils and ecosystems to Pb pollution varies greatly. In the present study, the comparative assessment of the resistance to Pb contamination in the forest, forest-steppe, steppe, and mountain-meadow soils of the Central Ciscaucasia and the Caucasus regions was conducted. There were 10 types and subtypes of objects from the forest, forest-steppe, steppe, and mountain-meadow soils which were selected for this study. The laboratory modeling of the effect of chemical soil contamination with lead (II) oxide (PbO) at different concentrations, 100, 1000, and 10,000 mg/kg, were introduced into the soil to check the microbiological, biochemical, and phytotoxic properties of the soil after 30 days of incubation. Soil resistance to Pb stress was assessed by the degree of the decrease in the most sensitive and informative biological indicators of the soil condition. It has been found that the forest-steppe and steppe soils showed a greater resistance than that of the forest and mountain-meadow soils. The regional maximum permissible concentration (rMPC) of Pb was developed for the first time, according to the degree of violation of the ecosystem functions of the soils. The forecast maps were developed for the deterioration of the soil condition during the Pb contamination at variable concentrations in the Central Ciscaucasia and the Caucasus regions. [ABSTRACT FROM AUTHOR]

Lead (Pb) is one of the most hazardous heavy-metal pollutants in the environment. However, the resistance of different soils and ecosystems to Pb pollution varies greatly. In the present study, the comparative assessment of the resistance to Pb contamination in the forest, forest-steppe, steppe, and mountain-meadow soils of the Central Ciscaucasia and the Caucasus regions was conducted. There were 10 types and subtypes of objects from the forest, forest-steppe, steppe, and mountain-meadow soils which were selected for this study. The laboratory modeling of the effect of chemical soil contamination with lead (II) oxide (PbO) at different concentrations, 100, 1000, and 10,000 mg/kg, were introduced into the soil to check the microbiological, biochemical, and phytotoxic properties of the soil after 30 days of incubation. Soil resistance to Pb stress was assessed by the degree of the decrease in the most sensitive and informative biological indicators of the soil condition. It has been found that the forest-steppe and steppe soils showed a greater resistance than that of the forest and mountain-meadow soils. The regional maximum permissible concentration (rMPC) of Pb was developed for the first time, according to the degree of violation of the ecosystem functions of the soils. The forecast maps were developed for the deterioration of the soil condition during the Pb contamination at variable concentrations in the Central Ciscaucasia and the Caucasus regions.

Abstract Background Knowledge of Ixodes ricinus tick distribution is critical for surveillance and risk management of transmissible tick-borne diseases such as Lyme borreliosis. However, as the ecology of I. ricinus is complex, and robust long-term geographically extensive distribution tick data are limited, mapping often relies on datasets collected for other purposes. We compared the modelled distributions derived from three datasets with information on I. ricinus distribution (quantitative I. ricinus count data from scientific surveys; I. ricinus presence-only data from public submissions; and a combined I. ricinus dataset from multiple sources) to assess which could be reliably used to inform Public Health strategy. The outputs also illustrate the strengths and limitations of these three types of data, which are commonly used in mapping tick distributions. Methods Using the Integrated Nested Laplace algorithm we predicted I. ricinus abundance and presence–absence in Scotland and tested the robustness of the predictions, accounting for errors and uncertainty. Results All models fitted the data well and the covariate predictors for I. ricinus distribution, i.e. deer presence, temperature, habitat, index of vegetation, were as expected. Differences in the spatial trend of I. ricinus distribution were evident between the three predictive maps. Uncertainties in the spatial models resulted from inherent characteristics of the datasets, particularly the number of data points, and coverage over the covariate range used in making the predictions. Conclusions Quantitative I. ricinus data from scientific surveys are usually considered to be gold standard data and we recommend their use where high data coverage can be achieved. However in this study their value was limited by poor data coverage. Combined datasets with I. ricinus distribution data from multiple sources are valuable in addressing issues of low coverage and this dataset produced the most appropriate map for national scale decision-making in Scotland. When mapping vector distributions for public-health decision making, model uncertainties and limitations of extrapolation need to be considered; these are often not included in published vector distribution maps. Further development of tools to better assess uncertainties in the models and predictions are necessary to allow more informed interpretation of distribution maps.

Background: Knowledge of Ixodes ricinus tick distribution is critical for surveillance and risk management of transmissible tick-borne diseases such as Lyme borreliosis. However, as the ecology of I. ricinus is complex, and robust long-term geographically extensive distribution tick data are limited, mapping often relies on datasets collected for other purposes. We compared the modelled distributions derived from three datasets with information on I. ricinus distribution (quantitative I. ricinus count data from scientific surveys; I. ricinus presence-only data from public submissions; and a combined I. ricinus dataset from multiple sources) to assess which could be reliably used to inform Public Health strategy. The outputs also illustrate the strengths and limitations of these three types of data, which are commonly used in mapping tick distributions. Methods: Using the Integrated Nested Laplace algorithm we predicted I. ricinus abundance and presence–absence in Scotland and tested the robustness of the predictions, accounting for errors and uncertainty. Results: All models fitted the data well and the covariate predictors for I. ricinus distribution, i.e. deer presence, temperature, habitat, index of vegetation, were as expected. Differences in the spatial trend of I. ricinus distribution were evident between the three predictive maps. Uncertainties in the spatial models resulted from inherent characteristics of the datasets, particularly the number of data points, and coverage over the covariate range used in making the predictions. Conclusions: Quantitative I. ricinus data from scientific surveys are usually considered to be gold standard data and we recommend their use where high data coverage can be achieved. However in this study their value was limited by poor data coverage. Combined datasets with I. ricinus distribution data from multiple sources are valuable in addressing issues of low coverage and this dataset produced the most appropriate map for national scale decision-making in Scotland. When mapping vector distributions for public-health decision making, model uncertainties and limitations of extrapolation need to be considered; these are often not included in published vector distribution maps. Further development of tools to better assess uncertainties in the models and predictions are necessary to allow more informed interpretation of distribution maps. [ABSTRACT FROM AUTHOR]

The rock partridge (Alectoris graeca saxatilis) is an alpine Galliform with high conservation value. Several factors, including parasitic helminths, play a role in population dynamics, and consequently in the conservation management of wild Galliformes. The aim of this study was to assess the epidemiological characteristics of Cheilospirura hamulosa (Nematoda, Acuarioidea) in the Rock partridge population in France. Machine learning modeling algorithms were applied to identify the environmental variables influencing parasite occurrence, and to map parasite presence probability. The present work is based on a long-term sampling (1987–2019) conducted in the French Alps. C. hamulosa was found with a prevalence (P) of 39% (Confidence Interval—CI 95%: 34–43), and mean intensity of 7.7 (7.8 sd). The highest prevalence (P: 67%, CI 95%: 54–80) was detected in the period 2005–2009. Latitude was the most important variable shaping the parasite distribution, followed by altitude, annual mean temperature, temperature seasonality, and the amount of precipitation of the coldest quarter. The area suitable for parasite presence included 73% of the French Alps. This work represents the first epidemiological surveillance on C. hamulosa infection in the rock partridge. It provides evidence of a high level of infection and identifies priority areas at higher infection risk, where a close monitoring of the rock partridge populations should be carried out.

Spatially explicit maps of wildlife habitat relationships have proven to be valuable tools for conservation and management applications including evaluating how and which species may be impacted by large scale climate change, ongoing fragmentation of habitat, and local land-use practices. Studies have turned to remote sensing datasets as a way to characterize vegetation for the examination of habitat selection and for mapping realized relationships across the landscape. Potentially one of the more difficult habitat types to try to characterize with remote sensing are the vertically and horizontally complex forest systems. Characterizing this complexity is needed to explore which aspects may represent driving and/or limiting factors for wildlife species. Active remote sensing data from lidar and radar sensors has thus caught the attention of the forest wildlife research and management community in its potential to represent three dimensional habitat features. The purpose of this review was to examine the applications of active remote sensing for characterizing forest in wildlife habitat studies through a keyword search within Web of Science. We present commonly used active remote sensing metrics and methods, discuss recent advances in characterizing aspects of forest habitat, and provide suggestions for future research in the area of new remote sensing data/techniques that could benefit forest wildlife studies that are currently not represented or may be underutilized within the wildlife literature. We also highlight the potential value in data fusion of active and passive sensor data for representing multiple dimensions and scales of forest habitat. While the use of remote sensing has increased in recent years within wildlife habitat studies, continued communication between the remote sensing, forest management, and wildlife communities is vital to ensure appropriate data sources and methods are understood and utilized, and so that creators of mapping products may better realize the needs of secondary users.

Abstract: Using remotely-sensed metrics to identify regions containing high animal diversity and/or specific animal species or guilds can help prioritize forest management and conservation objectives across actively managed landscapes. We predicted avian species richness in two mixed conifer forests, Moscow Mountain and Slate Creek, containing different management contexts and located in north-central Idaho. We utilized general linear models and an AIC model selection approach to examine the relative importance of a wide range of remotely-sensed ecological variables, including LiDAR-derived metrics of vertical and horizontal structural heterogeneities of both vegetation and terrain, and Landsat-derived vegetation reflectance indices. We also examined the relative importance of these remotely sensed variables in predicting nesting guild distributions of ground/understory nesters, mid-upper canopy nesters, and cavity nesters. All top models were statistically significant, with adjusted R2s ranging from 0.05 to 0.42. Regardless of study area, the density of the understory was positively associated with total species richness and the ground/understory nesting guild. However, the relative importance of ecological predictors generally differed between the study areas and among the nesting guilds. For example, for mid-upper canopy nester richness, the best predictors at Moscow Mountain included height variability and canopy density whereas at Slate Creek they included slope, elevation, patch diversity and height variability. Topographic variables were not found to influence species richness at Moscow Mountain but were strong predictors of avian species richness at the higher elevation Slate Creek, where species richness decreased with increasing slope and elevation. A variance in responses between focal areas suggests that we expand such studies to determine the relative importance of different factors in determining species richness. It is also important to note that managers using predictive maps should realize that models from one region may not adequately represent communities in other areas. [Copyright &y& Elsevier]

The article discusses a research study which identified the factors determining the large-scale seasonal abundance and species distribution of the common kestrel Falco tinnunculus in Central Spain. Results showed the importance of geographical variables to year-round kestrel distribution, while the patterns of longitude and latitude determine the breeding season abundance of kestrels. Agricultural variables such as cover of extensive dry coplands and extent of farmland mosaics were found to have predictive value in kestrel abundance. The researchers observed a negative correlation between breeding abundance and farmland environments, with parkland and scrub having a significant but complex relationships with kestrel abundance.

The article discusses a study concerning the modeling of ecological niche of three West Nile virus (WNV) vector mosquito species in Iowa. It is stated that three niche models were developed by using Ecological niche modeling (ENM) algorithms, Maximum Entropy Species Distribution Modeling (Maxent) and Genetic Algorithm for Rule-set Prediction (GARP). It further states that niche models created by the Maxent were the most useful indicator of WNV human incidence in Iowa.

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In spite of increasing application of presence-only models in ecology and conservation and the growing number of such models, little is known about the relative performance of different modelling methods, and some of the leading models (e.g. GARP and ENFA) have never been compared with one another. Here we compare the performance of six presence-only models that have been selected to represent an increasing level of model complexity [BIOCLIM, HABITAT, Mahalanobis distance (MD), DOMAIN, ENFA, and GARP] using data on the distribution of 42 species of land snails, nesting birds, and insectivorous bats in Israel. The models were calibrated using data from museum collections and observation databases, and their predictions were evaluated using Cohen's Kappa based on field data collected in a standardized sampling design covering most parts of Israel. Predictive accuracy varied between modelling methods with GARP and MD showing the highest accuracy, BIOCLIM and ENFA showing the lowest accuracy, and HABITAT and DOMAIN showing intermediate accuracy levels. Yet, differences between the various models were relatively small except for GARP and MD that were significantly more accurate than BIOCLIM and ENFA. In spite of large differences among species in prevalence and niche width, neither prevalence nor niche width interacted with the modelling method in determining predictive accuracy. However, species with relatively narrow niches were modelled more accurately than species with wider niches. Differences among species in predictive accuracy were highly consistent over all modelling methods, indicating the need for a better understanding of the ecological and geographical factors that influence the performance of species distribution models. [ABSTRACT FROM AUTHOR]

1. In recent years the use of species distribution models by ecologists and conservation managers has increased considerably, along with an awareness of the need to provide accuracy assessment for predictions of such models. The kappa statistic is the most widely used measure for the performance of models generating presence–absence predictions, but several studies have criticized it for being inherently dependent on prevalence, and argued that this dependency introduces statistical artefacts to estimates of predictive accuracy. This criticism has been supported recently by computer simulations showing that kappa responds to the prevalence of the modelled species in a unimodal fashion. 2. In this paper we provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce into ecology an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of kappa. We also compare the responses of kappa and TSS to prevalence using empirical data, by modelling distribution patterns of 128 species of woody plant in Israel. 3. The theoretical analysis shows that kappa responds in a unimodal fashion to variation in prevalence and that the level of prevalence that maximizes kappa depends on the ratio between sensitivity (the proportion of correctly predicted presences) and specificity (the proportion of correctly predicted absences). In contrast, TSS is independent of prevalence. 4. When the two measures of accuracy were compared using empirical data, kappa showed a unimodal response to prevalence, in agreement with the theoretical analysis. TSS showed a decreasing linear response to prevalence, a result we interpret as reflecting true ecological phenomena rather than a statistical artefact. This interpretation is supported by the fact that a similar pattern was found for the area under the ROC curve, a measure known to be independent of prevalence. 5. Synthesis and applications. Our results provide theoretical and empirical evidence that kappa, one of the most widely used measures of model performance in ecology, has serious limitations that make it unsuitable for such applications. The alternative we suggest, TSS, compensates for the shortcomings of kappa while keeping all of its advantages. We therefore recommend the TSS as a simple and intuitive measure for the performance of species distribution models when predictions are expressed as presence–absence maps. [ABSTRACT FROM AUTHOR]

We introduce the concept of the Mahalanobis distance to bioclimatic modeling. Specifically, we argue that climatic envelopes defined by the Mahalanobis distance produce more accurate predictions of species distribution than standard rectilinear envelopes (e.g. those produced by bioclim). We base our hypothesis on three rationales: (1) the climatic envelope generated by the Mahalanobis distance is oblique, and therefore, may cope with correlations and interactions among the climatic variables; (2) the Mahalanobis envelope is elliptic, and therefore, better reflects the principle of central tendency as expressed by niche theory; (3) Mahalanobian predictions are based on the whole data rather than on the outermost observations, and are therefore, less sensitive to outliers. We test our hypothesis using data on the distribution of 192 species of woody plants in Israel. Validation tests based on four measures of accuracy (sensitivity, specificity, overall accuracy and the Kappa statistic) support our hypothesis, and suggest that Mahalanobis models produce predictions that are significantly more accurate than those produced by corresponding rectilinear models. Additional simulation experiments demonstrate that the superiority of Mahalanobian models cannot be related to their elliptic shape, or their ability to cope with correlations among the climatic variables. Accordingly, our conclusion is that the prime advantage of Mahalanobian models originates from the fact that their climatic envelopes are defined using all the observations, as opposed to rectilinear envelopes that are founded on the outermost observations. [Copyright &y& Elsevier]

The rock partridge (Alectoris graeca saxatilis) is an alpine Galliform with high conservation value. Several factors, including parasitic helminths, play a role in population dynamics, and consequently in the conservation management of wild Galliformes. The aim of this study was to assess the epidemiological characteristics of Cheilospirura hamulosa (Nematoda, Acuarioidea) in the Rock partridge population in France. Machine learning modeling algorithms were applied to identify the environmental variables influencing parasite occurrence, and to map parasite presence probability. The present work is based on a long-term sampling (1987–2019) conducted in the French Alps. C. hamulosa was found with a prevalence (P) of 39% (Confidence Interval—CI 95%: 34–43), and mean intensity of 7.7 (7.8 sd). The highest prevalence (P: 67%, CI 95%: 54–80) was detected in the period 2005–2009. Latitude was the most important variable shaping the parasite distribution, followed by altitude, annual mean temperature, temperature seasonality, and the amount of precipitation of the coldest quarter. The area suitable for parasite presence included 73% of the French Alps. This work represents the first epidemiological surveillance on C. hamulosa infection in the rock partridge. It provides evidence of a high level of infection and identifies priority areas at higher infection risk, where a close monitoring of the rock partridge populations should be carried out. [ABSTRACT FROM AUTHOR]

Abstract: Geostatistical analysis was applied to the incidence of Aspergillus Section Nigri and A. carbonarius in Southern Europe and Israel for the 3-year period 2001–2003 to facilitate identification of regions of high risk from contamination with these fungi and production of ochratoxin. The highest incidence of black aspergilli was normally observed at harvesting. At this grape growth stage, spatial variability of black aspergilli was significantly related to latitude and longitude, showing a positive West–East and North–South gradient. Predictive maps of infected berries incidence were drawn and showed the same trend in the 3 years, but incidence was highest in 2003, followed by 2001 and 2002. The highest incidence was always observed in Israel, Greece and Southern France, associated with the highest incidence of A. carbonarius. Southern Spain and Southern Italy also had relevant incidence of black aspergilli. The thermo-wetness maps for the 3 years showed a trend similar to the incidence of black aspergilli. The coldest and wettest year was 2002, while 2003 was the hottest and driest, particularly during August, with Israel being the hottest and driest country, followed by Greece and Southern Italy. This indicates that meteorological conditions can contribute to explain spatial distribution variation of black aspergilli within the Mediterranean basin. [Copyright &y& Elsevier]