Distribution Model Research Articles

Research on the random generation model of 2D irregular aggregates in concrete based on fast grid region division method and its ground penetrating radar characteristics

Concrete can be regarded as a composite material with aggregates embedded in hardened slurries. The size, shape and distribution of aggregates directly affect multiple properties of concrete, including mechanical properties, ultrasonic field characteristics of concrete and ground penetrating radar (GPR) wave field characteristics. Therefore, conducting in-depth research on the random generation model of two-dimensional irregular aggregates is particularly important. This article introduces a fast grid region division method (FGRDM), which is an efficient reconstruction algorithm designed for two-dimensional concrete aggregate models. The algorithm takes into consideration the random micro-aggregate structure of concrete. A two-dimensional random distribution model of irregular aggregates in concrete was successfully established by generating concrete models with different particle size distributions and aggregate contents. Furthermore, the finite difference time domain method was used to analyze the GPR wave field of the random model. The significant influence of aggregate shape and distribution on the GPR wave field was discussed. Compared with existing two-dimensional irregular aggregate random generation models, the FGRDM proposed in this paper avoids computational redundancy and generates irregular aggregates at an increasingly fast speed. In the analysis of GPR data, electromagnetic waves are greatly affected by the shape and distribution of aggregates, highlighting the critical role of two-dimensional random distribution models in GPR data analysis. The paper uses FGRDM to generate random irregular aggregates to simulate the distribution of aggregates in actual concrete. Different diameter glass fiber reinforced polymer (GFRP) bars are inserted into the actual concrete and the numerical model to investigate the effect of aggregate size on the recognition of different diameter GFRP bars. The simulation results are generally consistent with the experimental results.

Multiple solutions of tungsten carbide-cobalt cutting tool turning performance optimization results by Taguchi's combined multiobjective approach analysis an exponentiated exponential Weibull Dagum distribution model

Manufacturers employ various techniques to enhance tool life and influence both production costs and surface finish quality. Recently, cryogenic treatment has gained attention for improving machining performance. In this study, tungsten carbide-cobalt (WC–Co) cutting tools underwent cryogenic treatment at −196 °C for 24 h followed by tempering at 200 °C for 2 h. Machinability characteristics including Tool Wear Rate and Surface Roughness (Ra) were assed for Cryo Treated (CT) cutting tool during spinning operations on Al6063 alloy. This evaluation was compared with an untreated tool. Results demonstrate that the CT tool significantly enhances tool longevity while ensuring a satisfactory surface finish. To optimize the performance of the CT tool in turning processes, multiobjective optimization techniques, namely Taguchi's Grey Relational Analysis and Technique for Order Preference by Similarity to Ideal Solution, were utilized. In this research exponentiated exponential Weibull Dagum (EEWD) model has been utilized with the goal of increasing the tractability of the DGM (Dagum) in modeling the real-world data. The main object of this work, an innovative generalization of the EEWD distribution of Quantile Function (QntFEEWD) has been derived with six parameters. This QntFEEWD is significantly influent in driving measures of position such as the Qutl (quartiles), Octl (octiles), Decl (deciles), Pecl (percentiles), and median of EEWD distribution. The T-R{.} framework has been recently used to generalize different distributions; however, the DGM distribution's viability has not been examined. The T-R{.} combines three distributions, with one acting as a standard distribution. Each distribution's combined potency, which is a weighted hazard function of the baseline distribution, would have more parameters but be able to handle dataset bimodality with great flexibility. In order to generalize the DGM distribution, this paper used the quantile function of the Weibull distribution. The current study presents the EEWD, a novel generalized six parameter (6P) model.

Distribution Characteristics of Trichiurus japonicus and Their Relationships with Environmental Factors in the East China Sea and South-Central Yellow Sea

The largehead hairtail (Trichiurus japonicus) is the most productive fish caught in China. In order to understand the seasonal distribution of T. japonicus in the East China Sea and the central and southern parts of the Yellow Sea, three species distribution models were used in this study, namely the random-forest model, K-nearest-neighbor algorithm, and gradient-ascending decision-tree model, based on the data of trawling surveys in the East China Sea and central and southern parts of the Yellow Sea from 2008 to 2009. Combined with a variance inflation factor and cross-check, a distribution model of T. japonicus was screened and constructed to analyze the influence of environmental factors on the distribution of T. japonicus in the East China Sea and central and southern parts of the Yellow Sea. The results showed that the random-forest model had the advantages of fitting effect and prediction ability among the three models. The analysis of this model showed that the water depth, bottom water temperature, and surface salinity had a great influence on the habitat distribution of T. japonicus. The relative resources of T. japonicus increased with the increase of bottom water temperature, reached the maximum at 23.8 °C, and first increased and then decreased with the increase of water depth and surface salinity, reaching the maximum when water depth is 72 m and surface salinity is 31.2%. This study also used the random-forest model to predict the spatial distribution of T. japonicus in the central and southern waters of the East China Sea and south-central Yellow Sea from 2008 to 2009, and the results showed that the predicted results were close to the actual situation. The research results can provide a reference for the exploitation and protection of T. japonicus resources in the East China Sea and the south-central Yellow Sea.

Assisted colonization of the near-eastern fire salamander (Salamandra infraimmaculata) in Iran: distribution modeling and landscape analysis

Assisted colonization of the near-eastern fire salamander (Salamandra infraimmaculata) in Iran: distribution modeling and landscape analysis

Estimating the Sampling Distribution of Posterior Decision Summaries in Bayesian Clinical Trials.

Bayesian inference and the use of posterior or posterior predictive probabilities for decision making have become increasingly popular in clinical trials. The current practice in Bayesian clinical trials relies on a hybrid Bayesian-frequentist approach where the design and decision criteria are assessed with respect to frequentist operating characteristics such as power and type I error rate conditioning on a given set of parameters. These operating characteristics are commonly obtained via simulation studies. The utility of Bayesian measures, such as "assurance," that incorporate uncertainty about model parameters in estimating the probabilities of various decisions in trials has been demonstrated. However, the computational burden remains an obstacle toward wider use of such criteria. In this article, we propose methodology which utilizes large sample theory of the posterior distribution to define parametric models for the sampling distribution of the posterior summaries used for decision making. The parameters of these models are estimated using a small number of simulation scenarios, thereby refining these models to capture the sampling distribution for small to moderate sample size. The proposed approach toward the assessment of conditional and marginal operating characteristics and sample size determination can be considered as simulation-assisted rather than simulation-based. It enables formal incorporation of uncertainty about the trial assumptions via a design prior and significantly reduces the computational burden for the design of Bayesian trials ingeneral.

Variation Characteristics of Actual Evapotranspiration and Uncertainty Analysis of Its Response to Local Climate Change in Arid Inland Region of China

Exploring the variation characteristics of actual evapotranspiration (ETa) and its response to climate change in the arid inland region of China is of great significance for strengthening regional water resources management and maintaining ecological environment security and stability. Taking the Dulan River Basin as the research area, based on the meteorological data from the Wulan Station and hydrological data from the Shanggaba Station from 1981 to 2020, the variation characteristics of ETa at the annual scale were analyzed. The ETa estimation model and joint distribution model of P and potential evapotranspiration (ET0) was constructed based on climate factors, and the uncertainty of ETa response to climate change was explored with the water balance method, multiple linear regression, marginal distribution function, Copula function, and Monte Carlo algorithm. The results showed that the multi-year mean value of ETa in the study area was 261.6 mm, and the interannual process showed an insignificant upward trend, and had no abrupt change during the period. There were two obvious main cycles, which were 19-year periodic changes on the 30-year time scale and 6-year periodic changes on the 9-year time scale. The ETa estimation model based on precipitation (P) and ET0 had good simulation accuracy. The optimal marginal distributions of P and ET0 were Pearson-III (P-III) distribution and Generalized Extreme Value (GEV) distribution, respectively. The Copula joint distribution probability density of P and ET0 was a symmetric saddle-shaped distribution. ETa showed an inverted ‘S’ distribution with the change in joint guarantee rate of P and ET0, ranging from 116.9 mm to 498.6 mm. ETa was an interval range under a certain joint guarantee rate. The research results can provide support for the assessment of ETa, and help to further understand the driving mechanism of climate change on ETa in the arid inland region of China.

A targeted study to determine the conservation status of a Data Deficient montane lizard, the Eungella shadeskink (Saproscincus eungellensis)

Context Combatting biodiversity loss is often hamstrung by a lack of species-specific knowledge. Species considered Data Deficient (DD) on the IUCN Red List are poorly understood and often neglected in conservation investment, despite evidence they are often threatened. Reptiles have the highest percentage of DD species for any terrestrial vertebrate group. Aims We aimed to assess the conservation status of the DD Eungella shadeskink (Saproscincus eungellensis), which is endemic to Eungella National Park, Queensland, Australia. Methods A combination of a targeted field survey, ecological studies, and species distribution modelling were used. Key results Saproscincus eungellensis typically occurred within 25 m of streams, at elevations between 700 and 1000 m. The species is thigmothermic, with a low active body temperature (~23–26°C) and was predominantly observed on rocks and fallen palm fronds. The species has a highly restricted distribution with an estimated Area of Occupancy of 36 km2 and Extent of Occurrence of 81.7 km2, comprising one location (defined by the threat of climate change) with an estimated 16,352–52,892 mature individuals. The main threats are fire, invasive alien species and climate change, with the species forecast to lose all suitable habitat by 2080 under all climate change scenarios. Conclusions The species meets listing criteria for Critically Endangered under Criterion B of the International Union for Conservation of Nature. Implications Our results support recent studies indicating that some DD species are highly threatened. Our approach provides a template for conducting targeted studies to determine the conservation status of DD species, especially those with restricted ranges.

Study and Analysis of the Thunder Source Location Error Based on Acoustic Ray-Tracing

Error analysis and estimation of thunder source location results is a prerequisite for obtaining accurate location results of thunder sources, which is of great significance for a deeper understanding of the physical process of lightning channel discharges. Most of the thunder source location algorithms are based on the simplified model of the straight-line propagation of acoustic waves to determine the location of the thunder source; however, the acoustic wave is affected by the inhomogeneity of the atmosphere medium in the propagation process and its acoustic ray will be bent. Temperature and humidity are the main factors affecting the vertical distribution of the velocity of sound in the atmosphere, therefore, it is necessary to study the changes in location errors under the models of uniform vertical distribution of temperature only and uniform vertical distribution of humidity only. This paper focuses on the theory of acoustic ray-tracing in neglecting the presence of the wind and the acoustic attenuation and the theoretical derivation of the location error of thunder source inversion for the three models is carried out by using MATLAB R2019b programming. Then, simulation analysis and comparative study on the variation law of thunder source location error with the height of the source, ground temperature, ground humidity, and array position under the three models are carried out. The results of the study show that the maximum location error can be obtained from the straight-line propagation model, the location error obtained from the model of uniform vertical distribution of temperature only is the second, and the location error obtained from the model of uniform vertical distribution of humidity only is the least and can be negligible compared to the first two models. In the trend of error variation, the variation of location error with temperature and humidity is relatively flat in the first two models; however, the variation of location error with the height of the thunder source is more drastic, which can be more than 80%. The location error obtained from the array inversion closer to the thunder source increases linearly with the height of the thunder source, the location error obtained from the more distant array inversion shows a fast-decreasing trend at the height of the thunder source from 500 to 3500 m, and a flat trend above 3500 m. The location error varies relatively smoothly with the height of the thunder source, the ground temperature, and the ground humidity in the model of uniform vertical distribution of humidity only. In addition, the position of the array also has an important effect on lightning location. The further the horizontal distance from the source, the greater the location error will be obtained in the first two models, and when the thunder source is at a low height and detected at a long distance, the location error will be very large, so relevant data should be modified in actual observation.

Distribution of the Culex pipiens mosquito in mainland Portugal: a geospatial modelling study

Abstract Background The mosquito Culex pipiens is a vector for several pathogens, such as the West Nile virus. As the most abundant mosquito in Portugal, understanding its spatial distribution can support vector-borne diseases control and robust public health planning, given the mainland’s favourable climate for this vector. Information on its spatial distribution at a national level is limited, especially its association with climate variables. Thus we aimed to predict the suitability of Culex pipiens distribution in mainland Portugal, considering climate factors. Methods We used presence records for adult and larvae of Culex pipiens mosquitoes sampled across mainland Portugal from January 2017 to October 2023, as part of the Portuguese Vector Surveillance Network program (REVIVE). Between May-October, in random locations and throughout the year in point-of-entry sites, adult mosquito collection used CDC light traps and BG-Sentinel traps, and larvae collection used dippers in breeding sites. Sampling bias was corrected by filtering presence sites, retaining one record per each cell grid of 1 km2. Climatic data (including temperature, precipitation and elevation) were used as predictors in a maximum entropy (MaxEnt) model of the Culex pipiens geographical distribution. Results From the dataset of 6859 records, 1416 unique sites were obtained after filtering. Suitable habitats seem to be mainly distributed on the coast, particularly in Minho, Porto and Lisbon metropolitan areas, and Tagus Valley, with precipitation as the most important predictor. Conclusions Most West Nile Virus case reports come from the south of Portugal, but the potential distribution of Culex pipiens covers the whole mainland territory, with seemingly higher distribution on the north coast. However, bias due to convenience sampling could be present. West Nile vector surveillance should be a priority across all regions to accurately assess transmission risk and implement effective control measures. Key messages • The suitability of Culex pipiens distribution covers the entire mainland of Portugal, with higher presence in the north. • The distribution of Culex pipiens in the country points towards a need for developing region-specific strategies regarding West Nile Virus prevention and control.

Mutualism: a model for managing risk in P2P sharing

Abstract This article explores how the risk structure currently adopted by the sharing economy, in particular the highly formalised and contrived systems of contract constructed by platforms, is largely constituted by the rules of property law. This effectively ties sharing activities to the old model of private property and its accompanying boundaries of privatised risk and value and undermines the efforts of collaboration to supersede those boundaries through peer-to-peer co-production of value. This article aims towards presenting an alternative model of risk distribution for the sharing economy that is more reflective of its collaborative nature. To this end, I will draw on ideas from commons and mutualism and propose the possibility of creative use of contracts to stipulate positive duties and obligations between collaborative partners as a device to construct a collective and/or mutual risk system.

Shining a Light on Equitable Solar Energy: Addressing Challenges and Enhancing Opportunities for Business Participation in Community Solar

Solar energy plays a vital role in achieving the United States’ decarbonization goals. Enabling community solar projects is an underutilized strategy for increasing renewable energy generation and offers potential solutions for addressing disparities in solar energy distribution. Although community solar use has been initiated through utility-scale platforms, less attention and efforts have gone into facilitating community solar programs that collaborate with businesses who use distributed solar (i.e., rooftop) panels. Engaging businesses in community solar programs provides opportunities for commercial buildings to expand solar energy panels, maximizing use of available space for solar installations and decreasing land-use for solar farms, increasing use and energy resiliency at the residential level, and improving equitable access. We propose three policy recommendations: (1) Renewing and expanding the Sunny Awards recognition program; (2) Modifying tax incentives for businesses to reduce costs of solar installation and encourage energy distribution; and (3) Introducing a new business model for energy distribution. These recommendations provide strategies and insights for encouraging businesses to implement community solar on a broader scale.

The Postseismic Deformation of the 6 July 2019 Mw 7.1 Ridgecrest Earthquake from Burst Overlap Interferometry, InSAR, and GNSS

Abstract The July 2019 Ridgecrest earthquake sequence consists of an Mw 6.4 foreshock and an Mw 7.1 mainshock, which ruptured a complex set of orthogonal faults in the eastern California shear zone. We measure the co- and postseismic deformation associated with this sequence using the Burst Overlap Interferometry (BOI) technique in addition to the commonly used Interferometric Synthetic Aperture Radar (InSAR). The BOI technique, which provides displacement in the satellite’s along-track direction, offers important information on the postseismic deformation that cannot be measured by traditional InSAR and is only sparsely measured by the Global Navigation Satellite System networks. The BOI data reveal up to 4 cm displacement in the along-track direction, 10 km north of the northern tip of the seismic rupture, and up to 3 cm displacement along the coseismically active faults. These results rule out the possibility of significant shallow afterslip near the mainshock hypocenter, suggesting that the previously suggested poroelastic rebound is likely to be the cause for the significant postseismic line of sight deformation near the mainshock rupture. Based on the aftershocks’ moment tensor distribution, surface rupture, and simple forward modeling, we propose that the postseismic deformation north of the Ridgecrest rupture is caused by an aseismic slip along a north-trending normal fault of the Ridgecrest rupture that was induced by the Ridgecrest earthquake. Furthermore, we observed that both deformation and seismic activity decay slower over time as the distance from the Coso geothermal area increases. This decay is influenced by the mechanical properties of the crust, which are affected by the increased heat flow at Coso and thus suppress deformation and seismicity, ultimately controlling their temporal evolution.

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.

Predictions of species distributions based only on models estimating future climate change are not reliable

Changes in climate and land use are the most often mentioned factors responsible for the current decline in species diversity. To reduce the effect of these factors, we need reliable predictions of future species distributions. This is usually done by utilizing species distribution models (SDMs) based on expected climate. Here we explore the accuracy of such projections: we use orchid (Orchidaceae) recordings and environmental (mainly climatic) data from the years 1901–1950 in SDMs to predict maps of potential species distributions in 1980–2014. This should enable us to compare the predictions of species distributions in 1980–2014, based on records of species distribution in the years 1901–1950, with real data in the 1980–2014 period. We found that the predictions of the SDMs often differ from reality in this experiment. The results clearly indicate that SDM predictions of future species distributions as a reaction to climate change must be treated with caution.

Habitat suitability and relative abundance of wild boars in the east‐central Tianshan Mountains, China

AbstractAs wild boar populations and their distribution ranges increase, human–wild boar conflicts have become increasingly prevalent in numerous regions across the globe. These conflicts have a profound impact on human livelihoods, resulting in significant economic losses. Understanding the habitat requirements and relative abundance of wild boars is crucial prior to implementing any conservation measures. However, studies on wild boar habitat and population in the central and eastern regions of the Tianshan Mountains in China are lacking. We assessed the activity patterns and relative abundance of wild boars in these areas and evaluated habitat suitability using a combination of camera trapping, line transects, species distribution modeling (maximum entropy model), and hierarchical abundance modeling (Bayesian N‐mixture model). We used 311 infrared cameras and 280 field‐based line transects to cover approximately 31,000 km² from September 2022 to May 2023 in the east‐central Tianshan Mountains. We used 240 wild boar distribution locations and 13 environmental predictors in the development of species distribution models. We also used species counts and associated environmental predictors in the N‐mixture model to estimate the relative abundance of wild boar. Wild boars were most active during crepuscular hours (1800), and relatively active in the diurnal period compared to the nocturnal period. The probability of wild boar occurrence increased with higher normalized difference vegetation index (NDVI), the minimum temperature of the coldest month, and annual temperatures below 39°C. Boars were most likely to be found in closed deciduous‐coniferous forests. The relative abundance of wild boars was positively affected by NDVI and negatively affected by the minimum temperature of the coldest month and temperature annual range. Based on our results, we suggest areas of management priority. In particular, extensive and intact habitat with substantial wild boar populations, such as the Banfanggou, the South Mountain of Urumqi, and the Hutubi, should be prioritized for long‐term wild boar population monitoring and management so the adverse impacts of increasing wild boar populations in the study region can be minimized.

Modeling the seasonal wildfire cycle and its possible effects on the distribution of focal species in Kermanshah Province, western Iran.

Predicting environmental disturbances and evaluating their potential impacts on the habitats of various plant and animal species is a suitable strategy for guiding conservation efforts. Wildfires are a type of disturbance that can affect many aspects of an ecosystem and its species. Therefore, through the integration of spatial models and species distribution models (SDMs), we can make informed predictions of the occurrence of such phenomena and their potential impacts. This study focused on five focal species, namely, the brown bear (Ursus arctos), wild goat (Capra aegagrus), wild sheep (Ovis orientalis), wildcat (Felis silvestris), and striped hyena (Hyaena hyaena). This study used MODIS active fire data and ensemble machine learning methods to model the risk of wildfire occurrence in 2023 for spring, summer, and autumn separately. This study also investigated the suitability of habitats for focal species via SDMs. The predicted probability maps for wildfire risk and habitat suitability were converted to binary values via the true skill statistic (TSS) threshold. The overlap of the habitat suitability map and wildfire occurrence areas was analyzed via GAP analysis. The area prone to fire in spring, summer and winter is equal to 9077.32; 10,199.83 and 13,723.49 KM2 were calculated, which indicates an increase in wildfire risk. Proximity to roads is one of the most important factors affecting the possible effects of wildfires in all seasons. Most fire occurrences are concentrated on agricultural lands, which, when integrated with other land use types, have wildfire potential in all seasons. The use of fire to destroy agricultural residues is a critical factor in the occurrence of wildfires. The distribution range of each focal species is considered the most important component of fire susceptibility. Hence, the suitable habitat for Hyaena hyaena in spring, summer, and autumn, with areas of 5.257, 5.856, and 6.889 km2 respectively, is the most affected by the possibility of fire. In contrast, these areas have the lowest values for Ovis orientalis, with 162, 127, and 396 km2 respectively. Therefore, species that are dependent on human-based ecosystems have the highest vulnerability to wildfire. Conservation efforts should focus on familiarizing farmers with methods of destroying agricultural residues as well as the consequences of intentional fires. The findings of this study can be used to mitigate the negative impacts of wildfire and protect the habitat of focal species.

A nonlinear filter based on GSK for relative navigation using relative orbital elements

Due to the sensitivity of the relative orbital elements model to the measurement noise, the non-stationary heavy-tailed noise(NSHT) induced by the time-varying environment during the relative navigation usually leads to filter divergence. To address this problem, a new nonlinear filter based on Gaussian-Student's-Multivariate K(GSK) mixture distribution is proposed in this paper. A Dirichlet stochastic mixture vector fusing Gaussian, Student's t, and Multivariate K distributions is introduced, thus proposing a GSK mixture distribution modeling measurement likelihood; then the Kullback-Leibler Divergence (KLD) of the true posteriori probability density function(PDF) and the approximate posteriori PDF are minimized by a variational Bayesian(VB) technique to solve for the state and parameter approximate a posteriori estimations, and finally a new nonlinear filter based on the GSK mixture distribution is derived for angles-only relative navigation in time-varying environments. Simulation outcomes indicate that the filter can realize state estimation in non-stationary states effectively with 45.16% higher estimation accuracy than the existing advanced filters.

Prediction of the potential Brazilian distribution for invasive mollusc Melanoides tuberculata (Müller, 1774), including a new record from Northern Coastal Region

The freshwater snail Melanoides tuberculata (Müller, 1774) native from Africa and Asia, recently spreads out over the world. Records of invasive species are extremely important, because it helps in future invasions mitigation. Here, we report for the first time the occurrence of M. tuberculata in a protected area of Northern Brazilian Coast and use predictive distribution modeling to know the potential Brazilian distribution for this species. The gastropod M. tuberculata were collected in the municipalities of Tutóia and Santo Amaro - Maranhão State. According to the results of the predictive map distribution for Brazil, the regions with the highest susceptibility are the northeast, southeast and midwest. Preciptation of month and Isothermalithy were de variables that most contribute to the species’ potential distribution. This species presented high to medium suitability of occurrence in 32 national protected areas. Futher studies are recommended to monitor the growth and dispersion, in order to determine its real impact.

A New SDM-Based Approach for Assessing Climate Change Effects on Plant–Pollinator Networks

Current methods for studying the effects of climate change on plants and pollinators can be grouped into two main categories. The first category involves using species distribution models (SDMs) to generate habitat suitability maps, followed by applying climate change scenarios to predict the future distribution of plants and pollinators separately. The second category involves constructing interaction matrices between plants and pollinators and then either randomly removing species or selectively removing generalist or specialist species, as a way to estimate how climate change might affect the plant–pollinator network. The primary limitation of the first approach is that it examines plant and pollinator distributions separately, without considering their interactions within the context of a pollination network. The main weakness of the second approach is that it does not accurately predict climate change impacts, as it arbitrarily selects species to remove without knowing which species will truly shift, decline, or increase in distribution due to climate change. Therefore, a new approach is needed to bridge the gap between these two methods while avoiding their specific limitations. In this context, we introduced an innovative approach that first requires the creation of binary climate suitability maps for plants and pollinators, based on SDMs, for both the current and future periods. This step aligns with the first category of methods mentioned earlier. To assess the effects of climate change within a network framework, we consider species co-overlapping in a geographic matrix. For this purpose, we developed a Python program that overlays the binary distribution maps of plants and pollinators, generating interaction matrices. These matrices represent potential plant–pollinator interactions, with a ‘0’ indicating no overlap and a ‘1’ where both species coincide in the same cell. As a result, for each cell within the study area, we can construct interaction matrices for both the present and future periods. This means that for each cell, we can analyze at least two pollination networks based on species co-overlap. By comparing the topology of these matrices over time, we can infer how climate change might affect plant–pollinator interactions at a fine spatial scale. We applied our methodology to Chile as a case study, generating climate suitability maps for 187 plant species and 171 pollinator species, resulting in 2906 pollination networks. We then evaluated how climate change could affect the network topology across Chile on a cell-by-cell basis. Our findings indicated that the primary effect of climate change on pollination networks is likely to manifest more significantly through network extinctions, rather than major changes in network topology.

Non-adaptedness and vulnerability to climate change threaten Plathymenia trees (Fabaceae) from the Cerrado and Atlantic Forest

Climate change is increasing species extinction risk. The ability of a species to cope with climate change can be quantified by projecting distribution models and by estimating the risk of non-adaptedness using genomic data. The Cerrado and the Atlantic Forest in Tropical South America are increasingly threatened by habitat loss and anthropogenic climate change. This work aims to evaluate the ecological and genomic vulnerability of Plathymenia taxa and its lineages, P. reticulata, a Cerrado species, and P. foliolosa, an Atlantic Forest species, to determine their current and future habitat suitability and the mismatch between current local adaptation with the expected climate changes. The species distribution models predicted a high range loss for the Plathymenia lineages. The genotype-environment association analyses showed that the Plathymenia lineages have populations adapted to different precipitation and temperature seasonality regimes. The genomic offset analyses predict a mismatch between local adaptations and future climate for the Plathymenia indicating a high risk of non-adaptedness, especially in the pessimistic scenario. Our results show an elevated extinction risk of the species due to climate change. We suggest reevaluating the extinction risk and management of the Plathymenia species separately based on their differences in vulnerability to climate change.