Climate and land use/land cover changes increasing habitat overlap among endangered crested ibis and sympatric egret/heron species.

Climate and land use/land cover (LULC) changes have far-reaching effects on various biological processes in wildlife, particularly interspecific interactions. Unfortunately, interspecific interactions are often overlooked when assessing the impacts of environmental changes on endangered species. In this study, we examined niche similarities and habitat overlaps between wild Crested Ibis and sympatric Egret and Heron species (EHs) in Shaanxi, China, using Ecological niche models (ENMs). We aimed to forecast potential alterations in habitat overlaps due to climate and LULC changes. The results showed that although EHs possess a broader niche breadth compared to the Crested Ibis, they still share certain niche similarities, as indicated by Schoener's D and Hellinger's I values exceeding 0.5, respectively. Notably, despite varying degrees of habitat reduction, the shared habitat area of all six species expands with the changes in climate and LULC. We suggest that with the climate and LULC changes, the habitats of sympatric EHs are likely to suffer varying degrees of destruction, forcing them to seek refuge and migrate to the remaining wild Ibis habitat. This is primarily due to the effective conservation efforts in the Crested Ibis habitat in Yangxian County and neighboring areas. Consequently, due to the niche similarity, they will share and compete for limited habitat resources, including food and space. Therefore, we recommend that conservation efforts extend beyond protecting the Crested Ibis habitat. It is crucial to control human activities that contribute to LULC changes to safeguard the habitats of both Crested Ibis and other sympatric birds.

[Response of branch attributes of Larix kaempferi to climate variables]. / 日本落叶松枝条属性特征对气候变量的响应.

We established a mixed-effects model incorporating climatic factors for the base diameter and length of the primary branches of Larix kaempferi using stepwise regression, based on climatic data from a total of 40 standard plots located in Xiaolongshan, Gansu Province, Changlinggang Forest Farm in Jianshi County, Hubei Province, and Dagujia Forest Farm in Qingyuan County, Liaoning Province, as well as the data from 120 L. kaempferi sample trees. Additionally, we created prediction charts for the fixed effects portion of the optimal mixed model to determine the relationship between climatic factors and base diameter and branch length, to explore the differential response of L. kaempferi branches to climatic variables. The results showed that the base diameter mixing model with annual mean temperature and water vapor deficit and the branch length mixing model with annual mean temperature had the best fitting effect, with R2 of 0.6152 and 0.6823, respectively. Based on the fixed effects prediction chart of the mixed model, the overall basal diameter showed an increasing trend with the increases of relative branch depth. The average basal diameter size was in an order of young-aged plantation

[Global quantification of the spatial variability and temporal stability of throughfall]. / æž—å† ç©¿é€é›¨ç©ºé—´å¼‚è´¨æ€§åŠå ¶æ—¶é—´ç¨³å®šæ€§ç‰¹å¾çš„å ¨çƒé‡åŒ–.

Spatial variability of throughfall (i.e. the non-uniform characteristics of throughfall at different canopy positions) and its temporal persistence (i.e. time stability) are related to the quantity and efficiency of soil moisture replenishment, and affect plant competition and community succession dynamics by affecting resource availability. We carried out a meta-analysis with 554 papers (from 2000 to 2022) retrieved from Web of Science and China National Knowledge Infrastructure (CNKI) based on keyword search, quantified and compared the amount, spatial heterogeneity, and temporal stability characteristics of penetrating rain in different climate zones and plant functional types. Our results that throughfall proportion was lower in arid regions (72.0%±13.6%) than humid (75.1%±9.3%) and semi-humid areas (79.9%±10.4%). Cold climates had lower values (74.1%±14.6%) than temperate (74.2%±7.5%) and tropical climates (80.9%±14.6%). Shrubs (68.9%±14.9%) generally had lower throughfall proportion than trees (76.7%±9.1%). Broad-leaved trees (75.2%±11.1%) and conifers (75.1%±9.9%) showed similar throughfall proportions, as did evergreen (76.7%±10.0%) and deciduous species (74.7%±11.9%). Additionally, spatial variability (coefficient of variation) did not significantly differ across rainfall zones, temperature zones, or vegetation types. The spatial distribution of throughfall was relatively stable. Canopy structure was the dominant factor affecting temporal stability of throughfall. However, there was a lack of comparison between typical geographic units (i.e. spatial units with basically consistent geographical environmental conditions) at various temporal scales. Future research should expand upwards to the summary of global spatial scale rules and downwards to the analysis of process based temporal scale mechanisms, to depict the dynamic distribution of penetrating rain and unify observation standards to enhance comparability of different studies, in order to efficiently promote research on canopy penetrating rain and provide ecological and hydrological basis for protecting nature, managing artificial activities, and restoring degraded ecosystems.

[Growth characteristic of statolith of Todarodes pacificus in the East China Sea in the La Niña year]. / 拉尼娜发生期东海太平洋褶柔鱼耳石生长特征.

To investigate the differences on morphological growth patterns of statolith of Todarodes pacificus in the East China Sea during La Niña and normal years, we analyzed the samples of T. pacificus collected in the East China Sea by Chinese light purse seine fishery fleets from February to April in 2020 (a normal year) and 2021 (a La Niña year). The results showed that total statolith length (TSL), lateral dome length (LDL), wing length (WL), and maximum width (MW) could be used as characterization parameters to representing the morphological growth of statolith. The characterization parameters of statolith in T. pacificus differed significantly between different climate years and between different genders. The values of those characterization parameters of statolith were greater in normal year than those in La Niña year, which in both years were larger in females, except for TSL in males in La Niña year. The statolith growth of males were faster than that of females in different climate years. TSL, LDL, and WL increased faster in normal year, while MW increased faster in La Niña year. The relative size of statolith gradually slowed down with the growth of individuals.

Mapping climate suitability index for rainfed cultivation of medicinal plants by developing an AI-based probabilistic framework.

The Climate Suitability Index (CSI) can increase agricultural efficiency by identifying the high-potential areas for cultivation from the climate perspective. The present study develops a probabilistic framework to calculate CSI for rainfed cultivation of 12 medicinal plants from the climate perspective of precipitation and temperature. Unlike the ongoing frameworks based on expert judgments, this formulation decreases the inherent subjectivity by using two components: frequency analysis and Particle Swarm Optimization (PSO). In the first component, the precipitation and temperature layers were prepared by calculating the occurrence probability for each plant, and the obtained probabilities were spatially interpolated using geographical information system processes. In the second component, PSO quantifies CSI by classifying a study area into clusters using an unsupervised clustering technique. The formulation was implemented in the Lake Urmia basin, which was distressed by unsustainable water resources management. By identifying clusters with higher CSI values for each plant, the results provide deeper insights to optimize cultivation patterns in the basin. These insights can help managers and farmers increase yields, reduce costs, and improve profitability.

Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection.

BACKGROUND: The government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space-time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022. METHODS: Spatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level. RESULTS: Malaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010-2011, and with P. vivax incidence in the north and central regions during 2012-2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south. CONCLUSIONS: Malaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

Repetition increases belief in climate-skeptical claims, even for climate science endorsers.

Does repeated exposure to climate-skeptic claims influence their acceptance as true, even among climate science endorsers? Research with general knowledge claims shows that repeated exposure to a claim increases its perceived truth when it is encountered again. However, motivated cognition research suggests that people primarily endorse what they already believe. Across two experiments, climate science endorsers were more likely to believe claims that were consistent with their prior beliefs, but repeated exposure increased perceptions of truth for climate-science and climate-skeptic claims to a similar extent. Even counter-attitudinal claims benefit from previous exposure, highlighting the insidious effect of repetition.

Human response to the Younger Dryas along the southern North Sea basin, Northwest Europe.

Currently in NW Europe little is known about the human response to the extensive cold reversal at the end of the Pleistocene, the Younger Dryas (ca. 12,850 till ca. 11,650 cal BP), mainly due to the poor chronological resolution of the archaeological sites belonging to the Ahrensburgian Culture. Here we present a series of 33 radiocarbon dates performed on the seminal cave site of Remouchamps, situated in the Belgian Meuse basin. Combined with a revision of the available radiocarbon evidence along the southern North Sea basin (Belgium, southern Netherlands, western Germany), it is suggested that the first half of the Younger Dryas, characterized as extremely cold and wet, faced a significant population reduction. Repopulation started around the middle of the Younger Dryas, from ca. 12,200 cal BP onward, probably in response to a slight climatic improvement leading to somewhat warmer summers. This might be considered a prelude to the subsequent population boost of the Early Holocene (Mesolithic).

Strain variation and anomalous climate synergistically influence cholera pandemics.

BACKGROUND: Explanations for the genesis and propagation of cholera pandemics since 1817 have remained elusive. Evolutionary pathogen change is presumed to have been a dominant factor behind the 7th "El Tor" pandemic, but little is known to support this hypothesis for preceding pandemics. The role of anomalous climate in facilitating strain replacements has never been assessed. The question is of relevance to guide the understanding of infectious disease emergence today and in the context of climate change. METHODOLOGY/PRINCIPAL FINDINGS: We investigate the roles of climate and putative strain variation for the 6th cholera pandemic (1899-1923) using newly assembled historical records for climate variables and cholera deaths in provinces of former British India. We compare this historical pandemic with the 7th (El Tor) one and with the temporary emergence of the O139 strain in Bangladesh and globally. With statistical methods for nonlinear time series analysis, we examine the regional synchrony of outbreaks and associations of the disease with regional temperature and rainfall, and with the El Niño Southern Oscillation (ENSO). To establish future expectations and evaluate climate anomalies accompanying historical strain replacements, climate projections are generated with multi-model climate simulations for different 50-year periods. The 6th cholera pandemic featured the striking synchronisation of cholera outbreaks over Bengal during the El Niño event of 1904-07, following the invasion of the Bombay Presidency with a delay of a few years. Accompanying anomalous weather conditions are similar to those related to ENSO during strain replacements and pandemic expansions into Africa and South America in the late 20th century. Rainfall anomalies of 1904-05 at the beginning of the large cholera anomaly fall in the 99th percentile of simulated changes for the regional climate. CONCLUSIONS/SIGNIFICANCE: Evolutionary pathogen change can act synergistically with climatic conditions in the emergence and propagation of cholera strains. Increased climate variability and extremes under global warming provide windows of opportunity for emerging pathogens.

Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data.

Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. We aimed to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models were trained using data from 2000 to 2018 and were evaluated using data from 2019 as testing data. During the training period, we set up 2-year sliding windows and a 1-year assessment period, along with the grid search method to optimize hyperparameters for each model. The best model for each region was selected using testing data, based on predictive precision and to prevent overfitting. Prediction intervals were then computed using conformal inference. The relative importance of all climatic variables was computed for the best model, and similar patterns in some of the seven regions were observed based on the selected model. Finally, reliable predictions were obtained for each of the seven regional hospitals.

Unmasking seasonal cycles in a high-input dairy herd in a hot environment: How climate shapes dynamics of milk yield, reproduction, and productive status.

This study aimed to predict the annual herd milk yield, lactation, and reproductive cycle stages in a high-input dairy herd in a zone with prolonged thermal stress. Also, the impact of climatic conditions on milk yield and productive and reproductive status was assessed. An autoregressive integrated moving average (ARIMA) model was used in data fitting to predict future monthly herd milk yield and reproductive status using data from 2014 to 2020. Based on the annual total milk output, the highest predicted percentage of milk yield based on the yearly milk production was in February (9.1%; 95% CI = 8.3-9.9) and the lowest in August (6.9%; 95% CI = 6.0-7.9). The predicted highest percentage of pregnant cows for 2021 was in May (61.8; 95% CI = 53.0-70.5) and the lowest for November (33.2%; 95% CI = 19.9-46.5). The monthly percentage of dry cows in this study showed a steady trend across years; the predicted highest percentage was in September (20.1%; CI = 16.4-23.7) and the lowest in March (7.5%; 4.0-11.0). The predicted days in milk (DIM) were lower in September (158; CI = 103-213) and highest in May (220; 95% CI = 181-259). Percentage of calvings was seasonal, with the predicted maximum percentage of calvings occurring in September (10.3%; CI = 8.0-12.5) and the minimum in April (3.2%; CI = 1.0-5.5). The highest predicted culling rate for the year ensuing the present data occurred in November (4.3%; 95% CI = 3.2-5.4) and the lowest in April (2.5%; 95% CI = 1.4-3.5). It was concluded that meteorological factors strongly influenced rhythms of monthly milk yield and reproductive status. Also, ARIMA models robustly estimated and forecasted productive and reproductive events in a dairy herd in a hot environment.

Spatial-temporal analysis of climate and socioeconomic conditions on cholera incidence in Mozambique from 2000 to 2018: an ecological longitudinal retrospective study.

OBJECTIVES: This study aims to assess both socioeconomic and climatic factors of cholera morbidity in Mozambique considering both spatial and temporal dimensions. DESIGN: An ecological longitudinal retrospective study using monthly provincial cholera cases from Mozambican Ministry of Health between 2000 and 2018. The cholera cases were linked to socioeconomic data from Mozambique Demographic and Health Surveys conducted in the period 2000-2018 and climatic data; relative humidity (RH), mean temperature, precipitation and Normalised Difference Vegetation Index (NDVI). A negative binomial regression model in a Bayesian framework was used to model cholera incidence while adjusting for the spatiotemporal covariance, lagged effect of environmental factors and the socioeconomic indicators. SETTING: Eleven provinces in Mozambique. RESULTS: Over the 19-year period, a total of 153 941 cholera cases were notified to the surveillance system in Mozambique. Risk of cholera increased with higher monthly mean temperatures above 24°C in comparison to the reference mean temperature of 23°C. At mean temperature of 19°C, cholera risk was higher at a lag of 5-6 months. At a shorter lag of 1 month, precipitation of 223.3 mm resulted in an 57% increase in cholera risk (relative risk, RR 1.57 (95% CI 1.06 to 2.31)). Cholera risk was greatest at 3 lag months with monthly NDVI of 0.137 (RR 1.220 (95% CI 1.042 to 1.430)), compared with the reference value of 0.2. At an RH of 54%, cholera RR was increased by 62% (RR 1.620 (95% CI 1.124 to 2.342)) at a lag of 4 months. We found that ownership of radio RR 0.29, (95% CI 0.109 to 0.776) and mobile phones RR 0.262 (95% CI 0.097 to 0.711) were significantly associated with low cholera risk. CONCLUSION: The derived lagged patterns can provide appropriate lead times in a climate-driven cholera early warning system that could contribute to the prevention and management of outbreaks.

Correlations between local geoclimatic variables and hatchling body size in the sea turtles Caretta caretta and Chelonia mydas.

It has been widely demonstrated that air and sand temperatures influence the anatomy of sea turtle hatchlings. We examined the impact of precipitation during the nesting season on the hatchling body size of loggerhead and green turtles from 37 beaches worldwide. Longitudinal data collected between 2012 and 2018 from Florida (US) and from a sample on Bõa Vista Island (Cabo Verde) carried out in 2019 showed that loggerhead body size at hatching was negatively correlated with precipitation, while precipitation was not correlated with hatchling body size in green turtles. A meta-analysis revealed that precipitation is positively correlated with hatchling mass in loggerhead turtles, while it is positively correlated with straight carapace length and width in green turtle hatchlings. The strongest influence of precipitation was found in the middle of the incubation period of loggerhead turtles in Cabo Verde, and we posit that this is due to an increase in the uptake of water for embryonic growth. These findings highlight the great importance of understanding the correlated effects of regional environmental variables, such as precipitation, on the development of sea turtle hatchlings and will have an impact on the evaluation of ongoing conservation and climate change discussions.

Deforestation-induced runoff changes dominated by forest-climate feedbacks.

Large-scale deforestation alters water availability through its direct effect on runoff generation and indirect effect through forest-climate feedbacks. However, these direct and indirect effects and their spatial variations are difficult to separate and poorly understood. Here, we develop an attribution framework that combines the Budyko theory and deforestation experiments with climate models, showing that widespread runoff reductions caused by the indirect effect of forest-climate feedbacks can largely offset the direct effect of reduced forest cover on runoff increases. The indirect effect dominates the hydrological responses to deforestation over 63% of deforested areas worldwide. This indirect effect arises from deforestation-induced reductions in precipitation and potential evapotranspiration, which decrease and increase runoff, respectively, leading to complex patterns of runoff responses. Our findings underscore the importance of forest-climate feedbacks for improved understanding and prediction of climate and hydrological changes caused by deforestation, with profound implications for sustainable management of forests and water resources.

Spatial mapping of dengue fever prevalence and its association with geo-climatic factors in Lahore, Pakistan.

A vector-borne disease of concern for global public health, dengue fever has been spreading its endemicity and several cases in recent years, particularly in Lahore Pakistan. Dengue transmission is influenced by geo-climatic conditions. This study aimed to map the spatial prevalence of dengue fever in Lahore and its association with geo-climatic factors during the epidemic of the year 2021. In this study, geo-climatic factors that could potentially encourage the growth of the virus are chosen for this study, and their temporal and spatial changeability relate to dengue cases. The objective of this study is to use meteorological, satellite data and Geographic Information System (GIS) techniques to map dengue outbreaks and identify the risk-prone areas by relating geo-climatic factors with dengue outbreaks. The dengue patients and their locations data were collected from the Directorate General of Health Services (DGHS) Lahore. This study uses Google Earth and Landsat-8 OLI/TIRs images to extract geo-climatic and land use parameters. The dot density maps technique was used to represent the spatiotemporal distribution of dengue cases. The hotspot analysis was applied to show the hotspots of dengue cases in district Lahore at the Union Council (UC) level. The Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), built-up area, population density, precipitation, and Land Surface Temperature (LST) are the factors employed. In this study, correlation was performed to test the significance between precipitation and the prevalence of dengue fever in Lahore. The results show that the incidence and prevalence of dengue fever month-wise at the UC level in Lahore. The distribution pattern of dengue outbreaks in the Lahore area and its demographic factors were found to be associated. It concludes that the increase in the spread of dengue fever is associated with the monsoon rains. The prevalence of dengue is associated with water bodies and high land surface temperature, but it does not represent any significant relation with vegetation cover and land use in Lahore during the year 2021. The study pinpointed the locations that are most susceptible and require care to prevent such outbreaks in the future.

Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning.

BACKGROUND: Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factors contribute to their ability to live in a variety of habitats, which have the potential to change over the years as the climate changes. Therefore, as new habitats emerge, the risk for new introductions of these diseases of interest to occur increases. The aim of this study was to model distributions for two primary vectors for BT and AHS (Culicoides imicola and Culicoides bolitinos) using random forest (RF) machine learning and explore the relative importance of environmental and anthropological factors in a region of South Africa with frequent AHS and BT outbreaks. METHODS: Culicoides capture data were collected between 1996 and 2022 across 171 different capture locations in the Western Cape. Predictor variables included climate-related variables (temperature, precipitation, humidity), environment-related variables (normalised difference vegetation index-NDVI, soil moisture) and farm-related variables (livestock densities). Random forest (RF) models were developed to explore the spatial distributions of C. imicola, C. bolitinos and a merged species map, where both competent vectors were combined. The maps were then compared to interpolation maps using the same capture data as well as historical locations of BT and AHS outbreaks. RESULTS: Overall, the RF models performed well with 75.02%, 61.6% and 74.01% variance explained for C. imicola, C. bolitinos and merged species models respectively. Cattle density was the most important predictor for C. imicola and water vapour pressure the most important for C. bolitinos. Compared to interpolation maps, the RF models had higher predictive power throughout most of the year when species were modelled individually; however, when merged, the interpolation maps performed better in all seasons except winter. Finally, midge densities did not show any conclusive correlation with BT or AHS outbreaks. CONCLUSION: This study yielded novel insight into the spatial abundance and drivers of abundance of competent vectors of BT and AHS. It also provided valuable data to inform mathematical models exploring disease outbreaks so that Culicoides-transmitted diseases in South Africa can be further analysed.

Spectral-genomic chain-model approach enhances the wheat yield component prediction under the Mediterranean climate.

In light of the changing climate that jeopardizes future food security, genomic selection is emerging as a valuable tool for breeders to enhance genetic gains and introduce high-yielding varieties. However, predicting grain yield is challenging due to the genetic and physiological complexities involved and the effect of genetic-by-environment interactions on prediction accuracy. We utilized a chained model approach to address these challenges, breaking down the complex prediction task into simpler steps. A diversity panel with a narrow phenological range was phenotyped across three Mediterranean environments for various morpho-physiological and yield-related traits. The results indicated that a multi-environment model outperformed a single-environment model in prediction accuracy for most traits. However, prediction accuracy for grain yield was not improved. Thus, in an attempt to ameliorate the grain yield prediction accuracy, we integrated a spectral estimation of spike number, being a major wheat yield component, with genomic data. A machine learning approach was used for spike number estimation from canopy hyperspectral reflectance captured by an unmanned aerial vehicle. The spectral-based estimated spike number was utilized as a secondary trait in a multi-trait genomic selection, significantly improving grain yield prediction accuracy. Moreover, the ability to predict the spike number based on data from previous seasons implies that it could be applied to new trials at various scales, even in small plot sizes. Overall, we demonstrate here that incorporating a novel spectral-genomic chain-model workflow, which utilizes spectral-based phenotypes as a secondary trait, improves the predictive accuracy of wheat grain yield.

Identifying the multiple drivers of cactus diversification.

Our understanding of the complexity of forces at play in the rise of major angiosperm lineages remains incomplete. The diversity and heterogeneous distribution of most angiosperm lineages is so extraordinary that it confounds our ability to identify simple drivers of diversification. Using machine learning in combination with phylogenetic modelling, we show that five separate abiotic and biotic variables significantly contribute to the diversification of Cactaceae. We reconstruct a comprehensive phylogeny, build a dataset of 39 abiotic and biotic variables, and predict the variables of central importance, while accounting for potential interactions between those variables. We use state-dependent diversification models to confirm that five abiotic and biotic variables shape diversification in the cactus family. Of highest importance are diurnal air temperature range, soil sand content and plant size, with lesser importance identified in isothermality and geographic range size. Interestingly, each of the estimated optimal conditions for abiotic variables were intermediate, indicating that cactus diversification is promoted by moderate, not extreme, climates. Our results reveal the potential primary drivers of cactus diversification, and the need to account for the complexity underlying the evolution of angiosperm lineages.

Climate influences the gut eukaryome of wild rodents in the Great Rift Valley of Jordan.

BACKGROUND: The mammalian gut microbiome includes a community of eukaryotes with significant taxonomic and functional diversity termed the eukaryome. The molecular analysis of eukaryotic diversity in microbiomes of wild mammals is still in its early stages due to the recent emergence of interest in this field. This study aimed to fill this knowledge gap by collecting data on eukaryotic species found in the intestines of wild rodents. Because little is known about the influence of climate on the gut eukaryome, we compared the composition of the gut eukaryotes in two rodent species, Mus musculus domesticus and Acomys cahirinus, which inhabit a transect crossing a temperate and tropical zone on the Jordanian side of the Great Rift Valley (GRV). METHODS: We used high-throughput amplicon sequencing targeting the 18S rRNA gene in fecal samples from rodents to identify eukaryotic organisms, their relative abundance, and their potential for pathogenicity. RESULTS: Nematodes and protozoa were the most prevalent species in the eukaryome communities, whereas fungi made up 6.5% of the total. Sixty percent of the eukaryotic ASVs belonged to taxa that included known pathogens. Eighty percent of the rodents were infected with pinworms, specifically Syphacia obvelata. Eukaryotic species diversity differed significantly between bioclimatic zones (p = 0.001). Nippostrongylus brasiliensis and Aspiculuris tetraptera were found to be present exclusively in the Sudanian zone rodents. This area has not reported any cases of Trichuris infections. Yet, Capillaria infestations were unique to the Mediterranean region, while Trichuris vulpis infestations were also prevalent in the Mediterranean and Irano-Turanian regions. CONCLUSIONS: This study highlights the importance of considering host species diversity and environmental factors when studying eukaryome composition in wild mammals. These data will be valuable as a reference to eukaryome study.