Precipitation Variability Research Articles

Seasonal and annual variations of sediment trapping and particulate organic carbon burial in Yellow River reservoirs

The Yellow River is distinguished by the highest sediment load in mainland China and significant siltation in artificial reservoirs along its main channel, reducing water availability, sediment trapping, and carbon burial in the hydrological project. Since 2002, the Water Sediment Regulation Scheme (WSRS) has been progressively implemented as a hydraulic management strategy to mitigate reservoir sedimentation in the middle-lower basin reservoirs. However, this substantial release of sediment and water has also affected river morphology, carbon burial, and sediment trapping.This research assesses the evolution of particulate organic carbon burial and sediment trapping in eight Yellow River reservoirs from 2002 to 2018, covering the upper and middle-lower basins and two reservoirs in the Yiluo River sub-basin. We calculated the annual and seasonal variations using hydrological data from 11 stations along the Yellow River. A hydrological framework was developed to calculate sediment trapping, and a Monte Carlo analysis was performed to estimate particulate organic carbon burial across all the evaluated reservoirs.We found that sediment trapping and carbon burial in upper basin reservoirs are most influenced by shifts in the precipitation regime, particularly by natural events such as severe droughts or heavy rainfall. A strong correlation was observed between annual precipitation variations and sediment load. In middle-lower basin reservoirs, the major artificial sediment regulation is strongly linked to a significant reduction in sediment trapping and particulate organic carbon burial. This impact is especially notable in the Yiluo River Sub-basin reservoirs, which have experienced a >90 % reduction compared to levels before the WSRS implementation.Finally, we highlight the consequences of climate change and artificial water management strategies in reservoirs, demonstrating how both affect their capacity for sediment trapping and impact their role as significant carbon sinks.

Global economic impact of weather variability on the rich and the poor

AbstractTemperature and precipitation variability and extremes impact production globally. These production disruptions will change with future warming, impacting consumers locally as well as remotely through supply chains. Due to a potentially nonlinear economic response, trade impacts are difficult to quantify; empirical assessments rather focus on the direct inequality impacts of weather extremes. Here, simulating global economic interactions of profit-maximizing firms and utility-optimizing consumers, we assess risks to consumption resulting from weather-induced production disruptions along supply chains. Across countries, risks are highest for middle-income countries due to unfavourable trade dependence and seasonal climate exposure. We also find that risks increase in most countries under future climate change. Global warming increases consumer risks locally and through supply chains. However, high-income consumers face the greatest risk increase. Overall, risks are heterogeneous regarding income within and between countries, such that targeted local and global resilience building may reduce them.

Untangling the environmental drivers of gross primary productivity in African rangelands

Precipitation variability is forecast to increase under climate change but its impacts on vegetation productivity are complex. Here, we use generalised additive models and remote sensing-derived datasets to quantify the effect of precipitation amount, distribution, and intensity on the gross primary productivity of dry rangelands across sub-Saharan Africa from 2000 to 2019 and differentiate these effects from other variables. We find that total precipitation is the primary driver of productivity, but that more variable rainfall has a small negative effect across vegetation types and rainfall regimes. Temperature and soil nitrogen also have strong effects, especially in drier rangelands. Shrublands and grasslands are more sensitive to environmental variability than savannas. Our findings support a model in which the main constraints on productivity are maintenance of soil moisture and minimisation of plant water stress. This highlights the risks of climate warming and increasing variability for productivity in water-limited grass and shrublands but suggests savannas may have greater resilience in Africa.

Evolution Characteristics of Heilongtan Spring Discharge and Its Response Law to Precipitation in Lijiang City, China

The contradiction between water supply and spring preservation issues is becoming increasingly apparent as Lijiang City develops. An investigation into the dynamic variations in the discharge rate of Heilongtan Spring in Lijiang City and the response law between the water level of the spring and precipitation is crucial for safeguarding the landscape water of Heilongtan Spring. This study employed linear regression analysis, Mann–Kendall (MK) mutation test, wavelet analysis, and vector autoregression (VAR) to examine the fluctuating pattern of the Heilongtan Spring discharge and the response of the Heilongtan Spring water level to precipitation in Lijiang City. Furthermore, the study discussed the influence of human activities on the alteration of Heilongtan Spring. The results indicate that the mean discharge rate of Heilongtan Spring is 0.94 m3/s, with an annual variation of 0.05 m3/s. The time series analysis reveals that the variation pattern of Heilongtan Spring discharge aligns with the precipitation trend in Lijiang City. Nevertheless, there is a distinction between the timing of the Heilongtan Spring discharge station point and the precipitation mutation point in Lijiang City. The significant primary cycle of spring discharge change occurs every 18 months, with a cycle length of 12 months. The vector autoregression (VAR) model demonstrates a lagged relationship between the water level of Heilongtan Spring and the precipitation in Lijiang City. Specifically, the water level of Heilongtan Spring has a four-month lag response to precipitation variability in Lijiang City. The results can provide a beneficial reference for preserving spring water and managing regional water resources.

Influence of El Niño southern oscillation on precipitation variability in Northeast Thailand

This study investigates the influence of El Niño Southern Oscillation (ENSO) on monthly precipitation anomaly (PPTA) in Northeast Thailand using 30 years (1993–2022) data obtained from 27 weather stations of the Thai Meteorological Department (TMD). Pearson correlation analysis elucidates the relationship between ENSO indices and PPTA. Results reveal significant correlations between ENSO indices and PPTA, providing insights into their intricate relationship. La Niña events have a stronger relationship with PPTA than El Niño events, with notable correlations observed.•Strong La Niña phases, PPTA tends to increase across Northeast Thailand when Niño 3, 3.4, and 4 are more strongly negative -0.64, -0.50, and -0.65, respectively. These correlations provide valuable insights into the hydrological influence of ENSO in Thailand.•After the ENSO event, there appears to be a 4–5 month lag period from the impact on PPTA that shows a higher correlation.•Wavelet transform coherence (WTC) analysis dissects the temporal and frequency-specific nature of the relationship between ENSO and PPTA found during the frequency of 2–7 years. These findings underscore the importance of studying regional variations in ENSO impacts for PPTA in Northeast Thailand.

Homogenization of fish assemblages in an endemic biodiversity hot spot: Evidence from 70‐year data from the Yun‐Gui Plateau, China

Abstract The combined impacts of anthropogenic activities and global climate changes threaten native fish communities. Such threats are particularly critical in biodiversity hot spot areas with many endemic species, as exemplified by the isolated Yun‐Gui Plateau, China. The risk of biotic homogenization is also expected to be high in such areas, but this risk and its underlying drivers remain poorly studied. Here, we established a database of fish distributions and used environmental filtering for spatial clusters (n = 3759) in river basins located at Yun‐Gui Plateau. This database was based on public data sources and a comprehensive review of relevant literature (n = 481) covering the period from 1950 to 2021. We divided the plateau into nine subbasins for spatial comparison and into two periods for temporal comparison—before and after the start of rapid economic growth development in 1978. We found: (a) a marginal increase in replacement (species exchange in different subbasins) and similarity index but a decrease in richness difference index, indicating homogenization and the loss of natural barriers across the entire plateau; (b) an increase in species richness accompanied by a discernible decline in phylogenetic diversity, also indicating homogenization; (c) the increase in species richness by species loss‐gain analyses was associated with climatic variables (temperature and precipitation), elevation and human activities in all subbasins. Temporal and spatial comparisons indicated that homogenization of fish assemblages in the Yun‐Gui Plateau was exacerbated by climatic and anthropogenic pressures. The observed homogenization of fish species is of great concern, and we call for systematic assessments and precautionary mitigation actions to maintain the uniqueness of biodiversity in the Yun‐Gui Plateau region.

Investigating the relative contribution from tropical Indo-Pacific SST to Asian monsoon precipitation variability using LIM

Abstract A critical issue is determining the factors that control the year-to-year variability in precipitation over Southern Asia. In this study, we employ a cyclo-stationary linear inverse model (CS-LIM) to quantify the relative contribution of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTA) to the interannual variability of the Asian monsoon, especially Indian summer monsoon rainfall (ISMR). Through a series of CS-LIM experiments, we isolate the impacts of the direct forcing from Pacific SSTAs, Indian Ocean SSTAs, and their interaction on Asian monsoon rainfall variability. Our results reveal distinct patterns of influence with the direct forcing from the Pacific (Indian) Ocean tending to enhance (reduce) the magnitude of precipitation variability, while the Indo-Pacific interaction acts to strongly damp the variability of Asian monsoon precipitation, especially over India. We further investigate these specific impacts on ISMR by analyzing the relationship between tropical Indo-Pacific SSTAs and the leading three empirical orthogonal functions (EOFs) of ISMR. The results from our CS-LIM experiments indicate that the direct forcing from ENSO enhances variability of the first and third EOFs, while the Indian Ocean SSTA opposes ENSO’s effects, which is consistent with previous studies. Our new results show that the tropical Indo-Pacific interaction strongly damps ISMR variability, which is due to the ENSO-induced Indian Ocean Dipole (IOD) opposing the direct impacts from ENSO on ISMR. Additionally, reduced ENSO amplitude and duration associated with the Indo-Pacific interaction may also contribute to the damping effect on ISMR, but this requires further study to understand the relevant mechanisms.

Cumulative effects of natural and anthropogenic processes on groundwater chemistry of a small karst island—case study of Vis (Croatia)

Many coastal and island communities depend on groundwater as the only source of freshwater, making it an invaluable resource. In the Mediterranean region, groundwater resources are highly vulnerable to natural and anthropogenic pressures, such as overexploitation, climate change, seasonal variations in precipitation, and seawater intrusion. Hence, an understanding of hydrogeological processes and groundwater chemistry is a basis for the sustainable management of coastal and island groundwater resources. Vis, a small and remote karst island in the Adriatic Sea, exhibits peculiar geological and hydrogeological settings, resulting in the island’s autonomous water supply. The current pumping capacity (maximum of 42 l/s) meets most of the demand, but intensive summer tourism and climate change exert high stress on groundwater resources during the dry season. Consequently, in the last decade, occasional reductions for consumers occurred. Monitoring of in situ physicochemical parameters and groundwater sampling for chemical and isotopic analyses were conducted from 2020 to 2023 at deep borewells, shallow dug wells, and springs. Hydrochemical interpretation indicated that groundwater chemistry was affected primarily by carbonate and sulfate rock dissolution, mixing with seawater, reverse ion exchange, and dedolomitization. The majority of groundwater samples exhibit Ca–HCO3 hydrochemical facies, followed by Na–Cl and mixed facies. The low percentage of seawater in the mixture indicated that seawater intrusion is not too extensive even during prolonged dry periods, implying a favorable hydrostatic regime with relatively small but sufficient groundwater reserves of the island’s aquifers, although the investigated period was characterized by significantly lower precipitation with respect to the 30-year average.

Contrasting survival strategies for seedlings of two northern conifer species to extreme droughts and floods.

Lowland northern white-cedar (Thuja occidentalis) forests are increasingly exposed to extreme droughts and floods that cause tree mortality. However, it is not clear the extent to which these events may differentially affect regeneration of cedar and its increasingly common associate, balsam fir (Abies balsamea). To test this, we measured how seedlings of cedar and fir were able to avoid, resist, and recover from experimental drought and flood treatments of different lengths (8-66days). Overall, we found that cedar exhibited a strategy of stress resistance and growth recovery (resilience) from moderate drought and flood stress. Fir, on the other hand, appears to be adapted to avoid drought and flood stress and exhibited overall lower growth resilience. In drought treatments, we found evidence of different stomatal behaviors. Cedar used available water quickly and therefore experienced more drought stress than fir but cedar was able to survive at water potentials > 3MPa below key hydraulic thresholds. On the other hand, fir employed a more conservative water use strategy and therefore avoided extremely low water potential. In response to flood treatments, cedar survival was higher and only reached 50% if exposed to 23.1days of flooding in contrast to only 7.4days to reach 50% mortality for fir. In both droughts and floods, many stressed cedar were able to maintain partially brown canopies and often survived the stress, albeit with reduced growth, suggesting a strategy of resistance and resilience. In contrast, fir that experienced drought or flood stress had a threshold-type responses and they either had full live canopies with little effect on growth or they died suggesting reliance on a strategy of drought avoidance. Combined with increasingly variable precipitation regimes, seasonal flooding, and complex microtopography that can provide safe sites in these forests, these results inform conservation and management of lowland cedar stands.

Exploring the Rhizospheric Microbial Communities under Long-Term Precipitation Regime in Norway Spruce Seed Orchard.

The rhizosphere is the hotspot for microbial enzyme activities and contributes to carbon cycling. Precipitation is an important component of global climate change that can profoundly alter belowground microbial communities. However, the impact of precipitation on conifer rhizospheric microbial populations has not been investigated in detail. In the present study, using high-throughput amplicon sequencing, we investigated the impact of precipitation on the rhizospheric soil microbial communities in two Norway Spruce clonal seed orchards, Lipová Lhota (L-site) and Prenet (P-site). P-site has received nearly double the precipitation than L-site for the last three decades. P-site documented higher soil water content with a significantly higher abundance of Aluminium (Al), Iron (Fe), Phosphorous (P), and Sulphur (S) than L-site. Rhizospheric soil metabolite profiling revealed an increased abundance of acids, carbohydrates, fatty acids, and alcohols in P-site. There was variance in the relative abundance of distinct microbiomes between the sites. A higher abundance of Proteobacteria, Acidobacteriota, Ascomycota, and Mortiellomycota was observed in P-site receiving high precipitation, while Bacteroidota, Actinobacteria, Chloroflexi, Firmicutes, Gemmatimonadota, and Basidiomycota were prevalent in L-site. The higher clustering coefficient of the microbial network in P-site suggested that the microbial community structure is highly interconnected and tends to cluster closely. The current study unveils the impact of precipitation variations on the spruce rhizospheric microbial association and opens new avenues for understanding the impact of global change on conifer rizospheric microbial associations.

Probability Distribution Analysis of Annual Maximum Precipitation in the Niger Delta: A Critical Step towards Effective Flood Risk Management

Aims: The study aimed to analyze historical maximum precipitation data from seven cities in the Niger Delta to assess vulnerability to extreme rainfall events, identify optimal probability distribution models for future predictions, and provide insights for flood risk management strategies. Study Design: This is a retrospective analytical study based on historical precipitation data. Place and Duration of Study: The study was conducted across seven major cities in the Niger Delta region of Nigeria, utilizing data collected over several decades. Methodology: Historical maximum precipitation data were statistically analyzed to determine the best-fit probability distribution models. The Kolmogorov-Smirnov (KS) test and Mean Squared Prediction Error (MSPE) were used to validate the suitability of distributions, including Log-Normal, Normal, Log-Pearson III, and Generalized Extreme Value (GEV) for each city. Return periods of 10, 25, 50, and 100 years were calculated to predict future precipitation trends and assess flood risks. Results: Significant variations in annual maximum precipitation were observed across the cities, with Calabar recording the highest peak at 4062.7mm. The Log-Normal distribution was the best fit for Akure and Calabar, while the Normal distribution best described rainfall in Benin City. Log-Pearson III was optimal for Owerri, Umuahia, and Uyo, and GEV best fitted Port Harcourt’s data. High P-values (>0.87) indicated good model fits across the cities. Return period analysis suggested greater risks of extreme precipitation events in coastal cities like Calabar and Port Harcourt. Conclusion: The study underscores the importance of tailored, city-specific flood management strategies in the Niger Delta to mitigate the impact of extreme rainfall events, particularly in the face of climate change.

Trace element partitioning controls on cave drip water compositions through prior calcite and aragonite precipitation

Speleothem trace element records can provide seasonal resolution climate reconstructions, but the interpretation can be challenging. Aragonite samples are understudied compared to calcite samples, despite that aragonite has 10x higher uranium concentrations and thus provide excellent dating precision. Here we present a high-resolution dataset of drip water trace elements (calcium, magnesium, strontium, barium, uranium) that are commonly affected by prior carbonate (calcite / aragonite) precipitation, a process often driven by effective rainfall. Our data suggest that prior calcite and aragonite precipitation can occur at the same drip site complicating the interpretation of strontium and uranium concentrations in aragonite speleothems. However, both processes can be distinguished with the “Sinclair test” through the trendline analysis of logarithmic relationships. Furthermore, our data show that aragonite speleothems slowly growing under relatively constant conditions are ideal to record variations in prior carbonate precipitation with its barium concentrations, independent from the prior carbonate precipitation mode.

Spatial and Temporal Variations’ Characteristics of Extreme Precipitation and Temperature in Jialing River Basin—Implications of Atmospheric Large-Scale Circulation Patterns

In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should be more deeply explored for its patterns and associations with climatic factors. In this study, based on the daily precipitation and atmospheric temperature datasets from 29 meteorological stations in JRB and its vicinity from 1960 to 2020, 10 extreme indices (6 extreme precipitation indices and 4 extreme temperature indices) were calculated. The spatial and temporal variations of extreme precipitation and atmospheric temperature were analyzed using Mann–Kendall analysis, to explore the correlation between the atmospheric circulation patterns and extreme indices from linear and nonlinear perspectives via Pearson correlation analysis and wavelet coherence analysis (WTC), respectively. Results revealed that among the six selected extreme precipitation indices, the Continuous Dry Days (CDD) and Continuous Wetness Days (CWD) showed a decreasing trend, and the extreme precipitation tended to be shorter in calendar time, while the other four extreme precipitation indices showed an increasing trend, and the intensity of precipitation and rainfall in the JRB were frequent. As for the four extreme temperature indices, except for TN10p, which showed a significant decreasing trend, the other three indices showed a significant increasing trend, and the number of low-temperature days in JRB decreased significantly, the duration of high temperature increased, and the basin was warming continuously. Spatially, the spatial variation of extreme precipitation indices is more obvious, with decreasing stations mostly located in the western and northern regions, and increasing stations mostly located in the southern and northeastern regions, which makes the precipitation more regionalized. Linearly, most of the stations in the extreme atmospheric temperature index, except TN10p, show an increasing trend and the significance is more obvious. Except for the Southern Oscillation Index (SOI), other atmospheric circulation patterns have linear correlations with the extreme indices, and the Arctic Oscillation (AO) has the strongest significance with the CDD. Nonlinearly, NINO3.4, Pacific Decadal Oscillation (PDO), and SOI are not the main circulation patterns dominating the changes of TN90p, and average daily precipitation intensity (SDII), maximum daily precipitation amount (RX1day), and maximum precipitation in 5 days (Rx5day) were most clearly associated with atmospheric circulation patterns. This also confirms that atmospheric circulation patterns and climate tend not to have a single linear relationship, but are governed by more complex response mechanisms. This study aims to help the relevant decision-making authorities to cope with the more frequent extreme climate events in JRB, and also provides a reference for predicting flood, drought and waterlogging risks.

Increasing extreme precipitation variability plays a key role in future record-shattering event probability

Climate events that break records by large margins are a threat to society and ecosystems. Climate change is expected to increase the probability of such events, but quantifying these probabilities is challenging due to natural variability and limited data availability, especially for observations and very rare extremes. Here we estimate the probability of precipitation events that shatter records by a margin of at least one pre-industrial standard deviation. Using large ensemble climate simulations and extreme value theory, we determine empirical and analytical record shattering probabilities and find they are in high agreement. We show that, particularly in high emission scenarios, models project much higher record-shattering precipitation probabilities in a changing relative to a stationary climate by the end of the century for almost all the global land, with the strongest increases in vulnerable regions in the tropics. We demonstrate that increasing variability is an essential driver of near-term increases in record-shattering precipitation probability, and present a framework that quantifies the influence of combined trends in mean and variability on record-shattering behaviour in extreme precipitation. Probability estimates of record-shattering precipitation events in a warming world are crucial to inform risk assessment and adaptation policies.

A novel framework for assessing the joint and relative impacts of precipitation and groundwater on ecological drought processes in Northwest China

AbstractEcological drought has emerged as a critical research topic within eco‐hydrology, driven by the increased drought risk associated with global warming. This study aims to unravel the hydrological processes driving ecological drought in Northwest China by (1) constructing standardized indices for precipitation, groundwater storage anomaly, and ecological water deficit to detect variation across different hydrological components; (2) developing a framework to assess the joint and relative impacts of precipitation and groundwater variations on ecological drought; and (3) identifying the primary hydrological drivers of ecological drought across different regions. The results indicate that the joint impact of precipitation and groundwater on ecological drought variation dominates approximately 60% of the area, primarily in arid and semi‐arid regions. The average contribution of this joint impact to the alleviation of ecological drought ranges between 0.26 and 0.43 across all seasons. Notably, groundwater scarcity, rather than precipitation variation, is the primary driver of ecological drought in regions such as southern Shaanxi, southeastern Gansu, and southern Qinghai, accounting for 12.7% to 21.8% of the total area. These insights into the complex hydrological processes underlying ecological drought have significant implications for water resource management and ecosystem conservation in drought‐prone regions. This research provides valuable information for mitigating drought impacts and protecting vulnerable ecosystems in Northwest China and similar regions worldwide.

Water budgets in an arid and alpine permafrost basin: Observations from the High Mountain Asia

Ground freeze‒thaw processes have significant impacts on infiltration, runoff and evapotranspiration. However, there are still critical knowledge gaps in understanding of hydrological processes in permafrost regions, especially of the interactions among permafrost, ecology, and hydrology. In this study, an alpine permafrost basin on the northeastern Qinghai‒Tibet Plateau was selected to conduct hydrological and meteorological observations. We analyzed the annual variations in runoff, precipitation, evapotranspiration, and changes in water storage, as well as the mechanisms for runoff generation in the basin from May 2014 to December 2015. The annual flow curve in the basin exhibited peaks both in spring and autumn floods. The high ratio of evapotranspiration to annual precipitation (>1.0) in the investigated wetland is mainly due to the considerably underestimated ‘observed’ precipitation caused by the wind-induced instrumental error and the neglect of snow sublimation. The stream flow from early May to late October probably came from the lateral discharge of subsurface flow in alpine wetlands. This study can provide data support and validation for hydrological model simulation and prediction, as well as water resource assessment, in the upper Yellow River Basin, especially for the headwater area. The results also provide case support for permafrost hydrology modeling in ungauged or poorly gauged watersheds in the High Mountain Asia.

Estimating Uncertainty in Simulated ENSO Statistics

AbstractLarge ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

Application of the AI2 Climate Emulator to E3SMv2's Global Atmosphere Model, With a Focus on Precipitation Fidelity

AbstractCan the current successes of global machine learning‐based weather simulators be generalized beyond 2‐week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10‐year simulations with a network trained on output from a physics‐based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea‐surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least 10 years with similarly small climate biases—a prerequisite to wider applicability. With an analysis that combines multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation data set with 10 fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine‐tuning experiments reveal ACE's intriguing ability to interpolate between distinct global climate models.

Climate change vulnerability and conservation strategies for tertiary relict tree species: Insights from landscape genomics ofTaxus cuspidata.

The unprecedented habitat fragmentation or loss has threatened the existence of many species. Therefore, it is essential to understand whether and how these species can pace with the environmental changes. Recent advantages in landscape genomics enabled us to identify molecular signatures of adaptation and predict how populations will respond to changing environments, providing new insights into the conservation of species. Here, we investigated the pattern of neutral and putative adaptive genetic variation and its response to changing environments in a tertiary relict tree species, Taxus cuspidata Sieb. et Zucc, which is distributed in northeast China and adjacent regions. We investigated the pattern of genetic diversity and differentiation using restriction site-associated DNA sequencing (RAD-seq) and seven nuclear microsatellites (nSSRs) datasets. We further explored the endangered mechanism, predicted its vulnerability in the future, and provided guidelines for the conservation and management of this species. RAD-seq identified 16,087 single nucleotide polymorphisms (SNPs) in natural populations. Both the SNPs and nSSRs datasets showed high levels of genetic diversity and low genetic differentiation in T. cuspidata. Outlier detection by F ST outlier analysis and genotype-environment associations (GEAs) revealed 598 outlier SNPs as putative adaptive SNPs. Linear redundancy analysis (RDA) and nonlinear gradient forest (GF) showed that the contribution of climate to genetic variation was greater than that of geography, and precipitation played an important role in putative adaptive genetic variation. Furthermore, the genetic offset and risk of non-adaptedness (RONA) suggested that the species at the northeast edge may be more vulnerable in the future. These results suggest that although the species has maintained high current genetic diversity in the face of recent habitat loss and fragmentation, future climate change is likely to threaten the survival of the species. Temperature (Bio03) and precipitation (Prec05) variables can be potentially used as predictors of response of T. cuspidata under future climate. Together, this study provides a theoretical framework for conservation and management strategies for wildlife species in the context of future climate change.

Prevalence, spatial distribution and risk mapping of Dirofilaria immitis in wild canids in southern Québec, Canada

Domestic dogs (Canis familiaris) and wild canids, including coyotes (Canis latrans) and red foxes (Vulpes vulpes), serve as definitive hosts for Dirofilaria immitis, a parasitic nematode causing the heartworm disease. Understanding infection risks in wildlife reservoirs in relation to environmental factors is crucial for assessing exposure risk in domestic dogs. The regional prevalence of D. immitis infection was estimated in trapped wild coyotes and red foxes across Québec, Canada. Spatial clusters of infection were detected using Kulldorff's spatial scan statistics. A series of logistic regression models predicting the D. immitis status in coyotes were built from heartworm development unit (HDU) estimates and cumulative precipitation variables over various time periods. Between October 2020 and March 2021, 421 coyotes and 284 red foxes were examined for the presence of D. immitis. The parasite was found in 43 coyotes and 1 red fox. A high-risk infection cluster was detected in coyotes in southwestern Québec. The best model included as sole predictor the average cumulative HDU contributing to risk of D. immitis in the three years preceding coyote capture. This model significantly predicted infection status with an area under the curve of 76.1%. The cumulative precipitation had no notable effect in any model. This study highlights a high prevalence of D. immitis in coyotes in Québec with regional differences correlated to temperature-derived predictors. The spatial risk of infection in this population likely represents the environmental risk of exposure to the parasite given that coyotes do not receive preventive treatment compared to domestic dogs. Our findings are important for veterinarians in the application of prevention strategies for heartworm disease in domestic dogs.