Probability Of Presence Research Articles

Assessment of fuzzy logic to enhance species distribution modelling of two cryptic wood boring beetle species in Australia.

Fuzzy logic presents a promising approach for Species Distribution Modelling by generating a value that can be used for comparative purposes termed 'environmental favourability'. In contrast to 'presence probability', 'environmental favourability' remains robust regardless of species prevalence. This characteristic facilitates effective comparisons across species with varying levels of prevalence. In this study, presence probability was predicted using three commonly used Species Distribution Models: Generalised Linear Model, Generalised Additive Modelling, and Boosted Regression Trees for two beetle species, Euwallacea fornicatus and Euwallacea perbrevis in Australia. Fuzzy logic was then employed to derive environmental favourability values based on these models. Additionally, Maxent modelling was included to compare prediction outputs and facilitate a comprehensive analysis. Model performance was evaluated using standard metrics (Area under the receiver operating characteristic curve, True statistical skill, Correct classification rate), as well as Hosmer-Lemeshow test. The research explored fuzzy similarity, fuzzy intersection and potential biotic interaction of these closely related borers, and revealed a favourable distribution pattern for Euwallacea fornicatus across Australia. This study supports the efficacy of fuzzy logic in Species Distribution Modelling and highlights the value of environmental favourability function in enhancing the comparative analysis of the geographical relationship across species. This approach offers a more nuanced perspective on Species Distribution Modelling.

Impact of Climate Change on Distribution of Endemic Plant Section Tuberculata (Camellia L.) in China: MaxEnt Model-Based Projection

Sect. Tuberculata, as one of the endemic plant groups in China, belongs to the genus Camellia of the Theaceae family and possesses significant economic and ecological value. Nevertheless, the characteristics of habitat distribution and the major eco-environmental variables affecting its suitability are poorly understood. In this study, using 65 occurrence records, along with 60 environmental factors, historical, present and future suitable habitats were estimated using MaxEnt modeling, and the important environmental variables affecting the geographical distribution of sect. Tuberculata were analyzed. The results indicate that the size of the its potential habitat area in the current climate was 1.05 × 105 km2, and the highly suitable habitats were located in Guizhou, central-southern Sichuan, the Wuling Mountains in Chongqing, the Panjiang Basin, and southwestern Hunan. The highest probability of presence for it occurs at mean diurnal range (bio2) ≤ 7.83 °C, basic saturation (s_bs) ≤ 53.36%, temperature annual range (bio7) ≤ 27.49 °C, −7.75 °C < mean temperature of driest quarter (bio9) < 7.75 °C, annual UV-B seasonality (uvb2) ≤ 1.31 × 105 W/m2, and mean UV-B of highest month (uvb3) ≤ 5089.61 W/m2. In particular, bio2 is its most important environmental factor. During the historical period, the potential habitat area for sect. Tuberculata was severely fragmented; in contrast, the current period has a more concentrated habitat area. In the three future periods, the potential habitat area will change by varying degrees, depending on the aggressiveness of emissions reductions, and the increase in the potential habitat area was the largest in the SSP2.6 (Low-concentration greenhouse gas emissions) scenario. Although the SSP8.5 (High-concentration greenhouse gas emissions) scenario indicated an expansion in its habitat in the short term, its growth and development would be adversely affected in the long term. In the centroid analysis, the centroid of its potential habitat will shift from lower to higher latitudes in the northwest direction. The findings of our study will aid efforts to uncover its originsand geographic differentiation, conservation of unique germplasms, and forestry development and utilization.

The effects of seasonal human mobility and Aedes aegypti habitat suitability on Zika virus epidemic severity in Colombia.

The Zika virus epidemic of 2015-16, which caused over 1 million confirmed or suspected human cases in the Caribbean and Latin America, was driven by a combination of movement of infected humans and availability of suitable habitat for mosquito species that are key disease vectors. Both human mobility and mosquito vector abundances vary seasonally, and the goal of our research was to analyze the interacting effects of disease vector densities and human movement across metapopulations on disease transmission intensity and the probability of super-spreader events. Our research uses the novel approach of combining geographical modeling of mosquito presence with network modeling of human mobility to offer a comprehensive simulation environment for Zika virus epidemics that considers a substantial number of spatial and temporal factors compared to the literature. Specifically, we tested the hypotheses that 1) regions with the highest probability of mosquito presence will have more super-spreader events during dry months, when mosquitoes are predicted to be more abundant, 2) regions reliant on tourism industries will have more super-spreader events during wet months, when they are more likely to contribute to network-level pathogen spread due to increased travel. We used the case study of Colombia, a country with a population of about 50 million people, with an annual calendar that can be partitioned into overlapping cycles of wet and dry seasons and peak tourism and off tourism seasons that drive distinct cyclical patterns of mosquito abundance and human movement. Our results show that whether the first infected human was introduced to the network during the wet versus dry season and during the tourism versus off tourism season profoundly affects the severity and trajectory of the epidemic. For example, Zika virus was first detected in Colombia in October of 2015. Had it originated in January, a dry season month with high rates of tourism, it likely could have infected up to 60% more individuals and up to 40% more super-spreader events may have occurred. In addition, popular tourism destinations such as Barranquilla and Cartagena have the highest risk of super-spreader events during the winter, whereas densely populated areas such as Medellín and Bogotá are at higher risk of sustained transmission during dry months in the summer. Our research demonstrates that public health planning and response to vector-borne disease outbreaks requires a thorough understanding of how vector and host patterns vary due to seasonality in environmental conditions and human mobility dynamics. This research also has strong implications for tourism policy and the potential response strategies in case of an emergent epidemic.

Are SNPs Linked to Somatic Cell Score Suitable Markers for the Susceptibility to Specific Mastitis Pathogens in Holstein Cows?

Mastitis in cattle is often caused by microorganism infections in the udder. The three most common pathogens are esculin-positive streptococci (SC+), coagulase-negative staphylococci (CNS), and Escherichia coli (E. coli). In a previous study, 10 SNPs were associated with somatic cell score and mastitis in diverse Holstein populations. We tested these SNPs for their effects on individual pathogen presence. Milk and pathogen samples of 3076 Holstein cows were collected from four farms. Samples were excluded if multiple pathogens were present at the same time. Records of the same pathogen within 14 days of each other were counted as one infection. This resulted in 1129 pathogen-positive samples. Cases and controls were in ratios of 20:80 for SC+, 8:92 for CNS, and 11:89 for E. coli. The lasso, backward, and forward methods were used to narrow down SNPs associated with pathogen presence. The suitability of the SNPs to separate the samples into cases or controls for each pathogen was indicated using ROC curves. The Cochran-Armitage (CAT) and the Jonckheere-Terpstra (JTT) tests evaluated the influence of the SNPs on pathogen presence. Finally, a generalised linear mixed model (GLMM) including fixed environmental effects and a random sire effect was fitted to the binary trait of pathogen presence to test for association. In total, six out of the 10 investigated SNPs showed associations with pathogen presence based on the forward method: Two SNPs each for SC+ (rs41588957, rs41257403) and CNS (rs109934030, rs109441194), and three for E. coli (rs109934030, rs41634110, rs41636878). The CAT and GTT tests linked four SNPs (rs41588957, rs41634110, rs109441194, rs41636878) to pathogen presence, two of which were confirmed with the GLMM (rs41634110, rs109441194), with effects on CNS and E. coli. The SNPs linked to CNS and those linked to E. coli explained 13.2% and 13.8% of the variance, compared to 19% and 18.4%, respectively, of the full model with all 10 SNPs. Half of the SNP genotypes previously linked to lower SCS also decreased the probability for pathogen presence and might therefore be targets not just for lower SCS but for a better pathogen resistance. Trial Registration: Not applicable, no new data were collected for this study.

Presence-only fisheries bycatch data produce biased species distribution predictions for Alcyonacean corals on British Columbia’s continental shelf and slope

Sedentary benthic species such as Alcyonacea corals form critical habitat for fishes and invertebrates. Assessing anthropogenic risks to these organisms requires unbiased, species distribution models (SDMs) that attempt to map probabilities of coral presence in relation to bio-physical ocean characteristics; however, in deep-water settings, the accuracy of SDMs is highly variable and dependent on spatial and taxonomic resolution. Here we investigated how data and model types affect SDM predictions of Alcyonacea probability of presence. We compared predictions from generalized additive models (GAMs) fitted to presence-absence observations over a stratified-random survey design with predictions from maximum entropy models (Maxent) fitted to presence-only bycatch records from commercial fisheries. We use a simulation analysis to show that both model types (i.e., GAM or Maxent) using presence-only bycatch data produced biased estimates of species distribution. Additionally, the maxent model fit using presence-only bycatch data produced biased estimates of performance metrics (AUC, TSS) that were overly optimistic. This study demonstrates a need for presence-absence data collected using a robust survey sampling design to fit SDMs that will better inform marine protected area placement while minimizing unnecessary economic losses, such as forgone fishing yield, due to sub-optimal placement.

Deep Learning With Optical Coherence Tomography for Melanoma Identification and Risk Prediction.

Malignant melanoma is the most severe skin cancer with a rising incidence rate. Several noninvasive image techniques and computer-aided diagnosis systems have been developed to help find melanoma in its early stages. However, most previous research utilized dermoscopic images to build a diagnosis model, and only a few used prospective datasets. This study develops and evaluates a convolutional neural network (CNN) for melanoma identification and risk prediction using optical coherence tomography (OCT) imaging of mice skin. Longitudinal tests are performed on four animal models: melanoma mice, dysplastic nevus mice, and their respective controls. The CNN classifies melanoma and healthy tissues with high sensitivity (0.99) and specificity (0.98) and also assigns a risk score to each image based on the probability of melanoma presence, which may facilitate early diagnosis and management of melanoma in clinical settings.

Archetypal Analysis of Kidney Allograft Biopsies Using Next-generation Sequencing Technology.

In kidney transplantation, molecular diagnostics may be a valuable approach to improve the precision of the diagnosis. Using next-generation sequencing (NGS), we aimed to identify clinically relevant archetypes. We conducted an Illumina bulk RNA sequencing on 770 kidney biopsies (540 kidney recipients) collected between 2006 and 2021 from 11 European centers. Differentially expressed genes were determined for 11 Banff lesions. An ElasticNet model was used for feature selection, and 4 machine learning classifiers were trained to predict the probability of presence of the lesions. NGS-based classifiers were used in an unsupervised archetypal analysis to different archetypes. The association of the archetypes with allograft survival was assessed using the iBox risk prediction score. The ElasticNet feature selection reduced the number of the genes from a range of 859-10 830 to a range of 52-867 genes. NGS-based classifiers demonstrated robust performances (precision-recall area under the curves 0.708-0.980) in predicting the Banff lesions. Archetypal analysis revealed 8 distinct phenotypes, each characterized by distinct clinical, immunological, and histological features. Although the archetypes confirmed the well-defined Banff rejection phenotypes for T cell-mediated rejection and antibody-mediated rejection, equivocal histologic antibody-mediated rejection, and borderline diagnoses were reclassified into different archetypes based on their molecular signatures. The 8 NGS-based archetypes displayed distinct allograft survival profiles with incremental graft loss rates between archetypes, ranging from 90% to 56% rates 7 y after evaluation (P < 0.0001). Using molecular phenotyping, 8 archetypes were identified. These NGS-based archetypes might improve disease characterization, reclassify ambiguous Banff diagnoses, and enable patient-specific risk stratification.

Landscape and meteorological determinants of malaria vectors' presence and abundance in the rural health district of Korhogo, Côte d'Ivoire, 2016-2018, and comparison with the less anthropized area of Diébougou, Burkina Faso.

Understanding how weather and landscape shape the fine-scale distribution and diversity of malaria vectors is crucial for efficient and locally tailored vector control. This study examines the meteorological and landscape determinants of (i) the spatiotemporal distribution (presence and abundance) of the major malaria vectors in the rural region of Korhogo (northern Côte d'Ivoire) and (ii) the differences in vector probability of presence, abundance, and diversity observed between that area and another rural West African region located 300 km away in Diébougou, Burkina Faso. We monitored Anopheles human-biting activity in 28 villages of the Korhogo health district for 18 months (2016 to 2018), and extracted fine-scale environmental variables (meteorological and landscape) from high-resolution satellite imagery. We used a state-of-the-art statistical modeling framework to associate these data and identify environmental determinants of the presence and abundance of malaria vectors in the area. We then compared the results of this analysis with those of a similar, previously published study conducted in the Diébougou area. The spatiotemporal distribution of malaria vectors in the Korhogo area was highly heterogeneous and appeared to be strongly determined and constrained by meteorological conditions. Rice paddies, temporary sites filled by rainfall, rivers and riparian forests appeared to be the larval habitats of Anopheles mosquitoes. As in Diébougou, meteorological conditions (temperatures, rainfall) appeared to significantly affect all developmental stages of the mosquitoes. Additionally, ligneous savannas were associated with lower abundance of malaria vectors. Anopheles species diversity was lower in Korhogo compared to Diébougou, while biting rates were much higher. Our results suggest that these differences may be due to the more anthropized nature of the Korhogo region in comparison to Diébougou (less forested areas, more agricultural land), supporting the hypothesis of higher malaria vector densities and lower mosquito diversity in more anthropized landscapes in rural West Africa. This study offers valuable insights into the landscape and meteorological determinants of the spatiotemporal distribution of malaria vectors in the Korhogo region and, more broadly, in rural west-Africa. The results emphasize the adverse effects of the ongoing landscape anthropization process in the sub-region, including deforestation and agricultural development, on malaria vector control.

A global analysis of nearshore and submarine springs: spatial distribution, controlling factors, and probability of presence

Coastal springs act as bi-directionally preferential flow paths between coastal aquifers and oceans. While these springs can supply coastal ecosystems with nutrients, they also present vulnerabilities such as contamination and seawater intrusion. Despite their significance, substantial knowledge gaps exist regarding coastal springs due to their complex hydrogeological nature. This study provides a comprehensive global assessment of coastal springs, focusing on their distribution, controlling factors, and likelihood of occurrence. A global dataset of known coastal springs was compiled and analyzed, revealing 66 % of identified springs in the Mediterranean region, mainly linked to karst systems. In contrast, fewer springs were noted in Africa, South America, and South Asia. Key factors influencing spring occurrence were examined using geostatistical methods and integrated into a multi-criteria decision-making framework to develop a Coastal Spring Probability Index (CSPI) along coastline. High-potential areas for coastal springs were identified in regions characterized by significant carbonate and volcanic rock formations, wetter climates, and active tectonic margins, such as southern Europe, the Caribbean, tropical islands, and eastern Asia. Conversely, regions with dry climates and high water demand, such as North Africa and South America, exhibited a lower likelihood of spring presence. These findings will serve as a baseline for local-scale studies and aimed at improving coastal spring inventories and establishing monitoring networks. It will contribute to vulnerability assessment studies, thereby enhancing the management of coastal groundwater resources. The study emphasizes the importance of multidisciplinary approaches to understand coastal spring dynamics and advocates for a strategic planning in groundwater management and conservation. Ongoing efforts to inventory and monitor coastal springs, coupled with targeted conservation measures, are essential for ensuring the long-term sustainability of coastal water resources and ecosystems.

Construction and evaluation of a combined diagnostic model for chronic periprosthetic joint infection based on serological tests

BackgroundEarly diagnosis of chronic periprosthetic joint infection (CPJI) is crucial for ensuring effective treatment and improving patient outcomes. However, many auxiliary diagnostic tests are challenging to implement on a large scale due to economic and technical constraints, making CPJI diagnosis difficult. This study aims to design and validate a combined diagnostic model based on commonly used serological tests to evaluate its diagnostic value for CPJI and develop a diagnostic nomogram.MethodsA retrospective study from January 2019 to February 2024 involving 170 patients undergoing knee and hip arthroplasty revision for CPJI and aseptic loosening (AL) was conducted across two medical centers. These patients were divided into the training set and validation set. Patients were categorized into CPJI and AL groups based on infection status. Serological tests conducted upon admission were collected, and single-factor and multi-factor logistic regression analyses were used to identify independent diagnostic factors for early infection. These factors were integrated to construct a nomogram model. The model's performance was evaluated using the receiver operating characteristic area under the curve (AUC), Hosmer–Lemeshow test, decision curve analysis (DCA), and calibration curve, with external validation conducted on the validation set.ResultsMultivariate logistic regression analysis showed that C-reactive protein (CRP), procalcitonin (PCT), and Platelet count/mean platelet volume ratio (PVR) were independent diagnostic factors for CPJI (p < 0.05). The AUCs for diagnosing CPJI using these individual factors were 0.806, 0.616, and 0.700 (p < 0.05), respectively, while their combined detection achieved an AUC of 0.861 (p < 0.05). The DCA clinical impact curve shows the combined model has good clinical utility when the threshold probability of infection presence is between 0.16 and 0.95. Similar results were obtained in the external validation cohort, with the combined detection having an AUC of 0.893.ConclusionThe combined diagnostic model of CRP, PCT, and PVR significantly improves the The combined diagnostic model of CRP, PCT, and PVR significantly improves the diagnostic performance for CPJI compared to individual serum biomarkers. It exhibits good sensitivity, specificity, and clinical applicability, providing valuable references for CPJI diagnosis.

Sliding window SA-CNN-based CFAR detector for extended target in shipborne HFSWR

ABSTRACT In shipborne high-frequency surface wave radar (HFSWR), the change of platform speed or heading will cause variations in the extension of the Doppler spectrum for vessel targets located in different directions, subsequently resulting in alterations to the signal-to-noise ratio (SNR). These alterations in vessel target echo present challenges for manual parameter adjustments in traditional constant false alarm rate (CFAR) methods for shipborne HFSWR, thereby hindering the maintenance of stable target detection capabilities. In this paper, a self-attention-convolutional neural network (SA-CNN)-based CFAR detector is proposed, which transforms the detection problem into signal structure classification. First, the extension characteristics of vessel target echoes resulting from changes in speed or heading of the shipborne platform are quantitatively analysed, thereby guiding the selection of an optimal sliding window and constructing input vectors for the neural network. Subsequently, the SA-CNN is designed to efficiently extract the structural features of the signal and accurately predict the probability of target presence. Finally, the Monte Carlo method is used to control the false alarm rate effectively. Simulation and real dataset verification demonstrate that the proposed method exhibits superior detection performance compared to traditional methods in shipborne HFSWR, especially for detecting extended targets.

The affinity of vascular plants and bryophytes to forest microclimate buffering

Abstract With recent advances in technology and modelling, ecologists are increasingly advised to use microclimate, not the usual coarse scale macroclimate based on weather stations, to better reflect the proximal conditions that species experience. This is especially relevant in forest ecosystems, where natural disturbances and management create substantial heterogeneity in microclimates. Under dense canopies, species may experience buffered (less extreme) microclimate temperatures relative to macroclimate, as well as increased relative humidity, reduced light and wind. Focusing on understorey plants, we investigated species response curves to the buffering capacity of the canopy layer, measured as the log‐transformed slope parameter of the microclimate to macroclimate linear relationship. If lower or higher than zero, microclimate temperatures are buffered or amplified, respectively, relative to macroclimate. During leaf‐on conditions (July–September 2021), we measured hourly microclimate temperatures in 157 plots across three temperate deciduous forests with contrasted macroclimates. We used paired hourly macroclimate measurements from nearby weather stations to derive the slope parameter, quantifying microclimate buffering. We surveyed vascular plant and bryophyte communities in 400 m2 plots centred on our microclimate sensors. Species were classified into three groups of forest affinity: core specialists; edge specialists; and generalists. We fitted generalized linear mixed‐effects models, by forest affinity group and by species, to obtain logistic response curves of the probability of occurrence against microclimate buffering. The species' optimum was computed as the microclimate effect that maximizes the species' probability of presence. We found contrasted microclimate preferences: Most bryophytes as well as the vascular plants classified as forest core specialists had an optimum in microclimate buffering, while forest edge specialists and generalists among vascular plants had an optimum in microclimate amplification. As canopies undergo increased disturbance frequency and intensity, more generalists and less forest core specialists might thus be expected in understorey communities, especially for bryophytes. Synthesis. Understorey plants have a species‐specific affinity to the forest microclimate, which we quantify for the first time. The investigation of species response curves to microclimate processes—buffering or amplification—can improve our understanding of the ecology of understorey plants, and help us anticipate their redistribution under climate change.

Assessing anatomical variations of the superior mesenteric artery via three-dimensional CT angiography and laparoscopic right hemicolectomy: a retrospective observational study.

This study aimed to analyse the anatomical relationships and differences between the superior mesenteric vessels and their branches by reviewing a laparoscopic right hemicolectomy surgery video and comparing it with preoperative three-dimensional computed tomography (3D-CT) angiography and to verify the accuracy of 3D-CT vascular reconstruction techniques. Surgical videos and preoperative imaging data of 52 patients undergoing laparoscopic right hemicolectomy were analysed to observe and summarize the probability of occurrence and adjacency of superior mesenteric vascular branches, and the lengths of specific sites of their branches were measured using the above two methods. Preoperative CT images and surgical video showed that the ileocolic artery (ICA) was present in 98.1% (51/52) and the ileocolic vein (ICV) was present in 100% (52/52), and ICA was present in 13.7% (7/51) of the ICV directly anteriorly, 13.7% (7/51) anteriorly superiorly, 3.9% (2/51) anteriorly inferiorly, 11.8% (6/51) directly posteriorly, 37.2% (19/51) post superiorly, and 19.7% (10/51) posteriorly inferiorly. In the surgical video, the probability of presence of the right colic artery (RCA) was 21.2% (11/52). On CT images, the RCA was present in 10 patients. The length of the origin of the middle colic artery (MCA) from its bifurcations was 2.33 ± 0.87 cm measured intraoperatively using a sterile isometric filament, and the length measured using 3D-CT vascular reconstruction was 2.36 ± 0.91 cm; the difference was not statistically significant (P = 0.348). The length of the MCA and ICA initiation points was 3.22 ± 0.75 cm measured intraoperatively using sterile isometric filaments and 3.36 ± 0.72 cm measured using 3D-CT vascular reconstruction, which was a statistically significant difference (P < 0.001). 3D-CT vascular reconstruction can accurately predict the vessels related to right hemicolectomy in most cases. It is an important method for preoperative prediction of superior mesenteric vessels, which can guide surgeons in the intraoperative vessel identification.

The initial impact of a predator exclosure sanctuary on small vertebrates in semi‐arid Western Australia

AbstractPredator exclosure reserves have become pivotal to conserving some threatened Australian mammalian fauna. However, the impact of predator exclosure reserves on resident (non‐target) small vertebrates is less understood. Given the potential ecological consequences of fencing, and the contribution of small vertebrates to ecosystem processes, we investigated species' occupancy, and site colonization and extinction dynamics, inside and outside of a predator exclosure fenced area for nine small vertebrates (six reptile, three mammal). We conducted vertebrate surveying 4 years (2011–2014) pre‐ and 2 years (2015–2016) post‐fence construction at the Australian Wildlife Conservancy Mt Gibson Sanctuary, in the semi‐arid, critically endangered Wheatbelt Eucalypt Woodlands of Western Australia, to assess spatial and temporal trends in small mammal and reptile populations. Of nine species modelled, one reptile (Diplodactylus pulcher) decreased in occupancy inside of the fence following construction. We detected no negative impact of the predator exclosure fence on the remaining eight species. One native reptile species (Lerista kingi) and an introduced rodent (Mus musculus) increased occupancy inside (but not outside) the fenced area. Spatial and temporal occupancy inside and outside of the fenced area for all other modelled species was stable. Biotic (habitat) factors and stochastic interannual detectability were the most significant drivers of species' occupancy. Species‐specific habitat associations were partially determined by site‐based presence and absence probabilities. This suggests that the predator exclosure sanctuary either benefitted or had minimal impact on all but one modelled species. These results may be a useful reference for future fencing projects aimed at both conservation and land management.

Application of machine learning classification algorithms to predict probability of channel presence from seismic data

The article analyzes the application of various classification algorithms (Random Forest, Extra Trees, Gradient Boosting, Multi-layer Perceptron, Gaussian Naive Bayes, Voting Classifier) based on supervised machine learning, both for individual seismic attributes (amplitude, phase, frequency) and for a set of seismic attributes calculated in the interval of channel system development for probabilistic assessment of presence of channels. The object of the study is the deposits of the Tyumen suite of a group of deposits located in the Khanty-Mansiysk Autonomous Okrug. The purpose of this work is to find the most effective seismic attributes and machine learning classification methods to optimize the methodology and improve the accuracy of forecasting the probability of channel system presence.

Habitat Suitability of Ixodes ricinus Ticks Carrying Pathogens in North-East Italy.

Ixodes ricinus ticks are ubiquitous in Europe, including in North-East Italy. These ticks are important vectors of several zoonotic pathogens of public health relevance. In this study, the habitat suitability range of I. ricinus ticks infected with zoonotic pathogens was predicted in North-East Italy, and relevant spatial predictors were identified. In 2015-2021, ticks were collected at 26 sampling sites in the study area. The collected ticks were screened for the presence of pathogens using PCR assays. For Borrelia, Rickettsia and Anaplasma/Ehrlichia species, data allowed for ecological niche modelling using Maxent. Environmental determinants potentially related to tick habitat suitability were used as model inputs. Predicted suitable habitat distributions revealed hotspots of the probability of pathogen presence in I. ricinus ticks mainly in the central and upper parts of the study area. Key environmental predictors were temperature, rainfall and altitude, and vegetation index for specific pathogens (Rickettsia and Anaplasma/Ehrlichia species). Increased risk of exposure to tick-borne pathogens upon tick bites in the predicted hotspot areas can, therefore, be expected. This provides useful information for public health risk managers in this and other similar regions.

Are Nature-based Solutions for Built Heritage Conservation Resilient to Climate Change? The Response of Grass-based Soft Caps in Britain and Ireland to Future Climate Scenarios

ABSTRACT Nature-based solutions (NbS) offer an exciting opportunity to work with nature to conserve heritage sites and objects. Soft capping is a novel form of NbS used commonly in Britain and Ireland to help conserve ruined and free-standing walls; soil and vegetation is placed on the wall head to help reduce deterioration by buffering thermal fluctuations and regulating moisture. While this conservation practice has proved cheaper and more effective at reducing deterioration than traditional hard capping, soft caps need to be resilient to future environmental changes. Previous research has shown that the sedum species commonly used in soft caps are vulnerable to climate change. Thus, following feedback from an interactive webinar with practicing heritage professionals, here we assess the climate resilience of four grass species commonly found in western Europe as possible soft-capping species: Festuca rubra (red fescue), Poa pratensis (smooth meadow-grass), Catapodium rigidum (fern-grass) and Poa annua (annual meadow-grass). We use species distribution modelling (Maxent) to assess the likelihood of species survival by mid and end century based on presence probabilities derived using three climate models (HadGEM3-GC31-LL, IPSL-CM6A-LR, and MIROC6). Results show that all four species are resilient under mid-century projections, and P. annua proved resilient under all scenarios. Therefore, using turf-based soft caps incorporating one or more of these grass species could enhance the future resilience of this conservation technique. Given the growing interest in the use of nature-based approaches to heritage conservation, our study represents an important attempt to account for and adapt to a changing climate.

Host preference explains the high endemism of ectomycorrhizal fungi in a dipterocarp rainforest.

Ectomycorrhizal (ECM) fungi are important tree symbionts within forests. The biogeography of ECM fungi remains to be investigated because it is challenging to observe and identify species. Because most ECM plant taxa have a Holarctic distribution, it is difficult to evaluate the extent to which host preference restricts the global distribution of ECM fungi. To address this issue, we aimed to assess whether host preference enhances the endemism of ECM fungi that inhabit dipterocarp rainforests. Highly similar sequences of 175 operational taxonomic units (OTUs) for ECM fungi that were obtained from Lambir Hill's National Park, Sarawak, Malaysia, were searched for in a nucleotide sequence database. Using a two-step binomial model, the probability of presence for the query OTUs and the registration rate of barcode sequences in each country were simultaneously estimated. The results revealed that the probability of presence in the respective countries increased with increasing species richness of Dipterocarpaceae and decreasing geographical distance from the study site (i.e. Lambir). Furthermore, most of the ECM fungi were shown to be endemic to Malaysia and neighbouring countries. These findings suggest that not only dispersal limitation but also host preference are responsible for the high endemism of ECM fungi in dipterocarp rainforests. Moreover, host preference likely determines the areas where ECM fungi potentially expand and dispersal limitation creates distance-decay patterns within suitable habitats. Although host preference has received less attention than dispersal limitation, our findings support that host preference has a profound influence on the global distribution of ECM fungi.

Impacts of forest extent, configuration and landscape context on presence of declining breeding Eurasian curlew Numenius arquata and implications for planning new woodland

In response to the combined impacts of the climate and biodiversity crises, as well as for timber security and increased recreational access to green spaces, there is a global drive to increase tree cover. In the UK, an estimated 1.5 million ha of afforestation are required to meet its carbon net-zero emissions targets (Committee on Climate Change, 2018). Despite the potential benefits, careful consideration must be given to the impacts of woodland creation on species adapted to open habitats. To investigate potential risks and mitigation for the IUCN Near Threatened Eurasian curlew Numenius arquata, a national spatially extensive field-scale dataset was used to investigate the relationships between curlew presence during the breeding season and a range of forest and landscape variables at two different spatial scales (0.5 km and 1 km). Variables included forest extent and configuration, and interaction between forests and extent of semi-natural open habitats, moorland management and topography. At both spatial scales, a negative relationship existed between extent of forest and the probability of curlew presence, and at 1 km, between probability of presence and the number of forest patches. However, these negative patterns depended on landscape context and were reduced where there was a greater quantity of semi-natural open habitat, such as moorland or rough grassland, and moorland management present. Overall, the findings emphasise the need to consider the impacts of woodland creation projects on species adapted to open habitats. However, the results suggest that these impacts can be influenced by landscape. These results could help inform decisions regarding the appropriateness of woodland creation in different landscapes and possible mitigation measures that could be applied against the risks created by afforestation.

Environmental Factors Associated With Fish Reproduction in Regulated Rivers

ABSTRACTHumans have extensively altered rivers to accommodate anthropogenic uses. Dams modify river flow and temperature regimes important for lotic fish reproduction. Yet, assessments of fish production in relation to environmental conditions in regulated rivers are lacking but are needed to guide experimental environmental flows. We evaluated the effects of water temperature and discharge on larval Catostomidae, Sciaenidae, and Clupeidae production to inform environmental flow management. We sampled ichthyoplankton from April through June on the Des Moines and Iowa rivers prior to (2014–2015) and after (2021–2022) an experimental environmental flow was incorporated on the Des Moines River. We used a hurdle model to assess the effects of water temperature, discharge, and discharge variation on larval presence (logistic regression) and density (linear regression). Larval Catostomidae were captured once water temperatures exceeded 15°C, Sciaenidae appeared when water temperature surpassed 18°C, while Clupeidae appeared when water temperature exceeded 20°C. The probability of larval Sciaenidae and Clupeidae presence increased with discharge variation while densities were both positively associated with discharge and discharge variation. The probability of Sciaenidae and Catostomidae larval presence increased with water temperature. Interactions between water temperature and discharge influenced Clupeidae presence and Catostomidae density. The probability of Clupeidae presence increased with discharge at warmer water temperatures. Catostomidae densities increased with discharge at cool water temperature (13°C) and decreased with discharge at warm (25°C) temperatures. Our results provide information about the effects of discharge, discharge variation, and water temperature driving larval fish production in anthropogenically altered rivers to guide environmental flow management.