Climatic Predictors Research Articles

Insights and Challenges for the Development of Mosquito Control Action Thresholds Using Historical Mosquito Surveillance and Climate Datasets.

Strategies to advance action threshold development can benefit both civilian and military vector control operations. The Anastasia Mosquito Control District (AMCD) has curated an extensive record database of surveillance programs and operational control activities in St. Johns County, Florida, since 2004. A thorough exploratory data analysis was performed on historical mosquito surveillance and county-wide climate data to identify climate predictors that could be used in constructing proactive threshold models for initiating control of Aedes, Culex, and Anopheles vector mosquitoes. Species counts pulled from Centers for Disease Control and Prevention (CDC) light trap (2004-2019) and BG trap (2014-2019) collection records and climate parameters of temperature (minimum, maximum, average), rainfall, and relative humidity were used in two iterations of generalized linear models. Climate readings were incorporated into models 1) in the form of continuous measurements, or 2) for categorization into number of "hot," "wet," or "humid" days by exceedance of selected biological index threshold values. Models were validated with tests of residual error, comparison of model effects, and predictive capability on testing data from the two recent surveillance seasons 2020 and 2021. Two iterations of negative binomial regression models were constructed for 6 species groups: container Aedes (Ae. aegypti, Ae. albopictus), standing water Culex (Cx. nigripalpus, Cx. quinquefasciatus), floodwater Aedes (Ae. atlanticus, Ae. infirmatus), salt-marsh Aedes (Ae. taeniorhyncus, Ae. sollicitans), swamp water Anopheles (An. crucians), and a combined Total Mosquitoes group. Final significant climate predictors varied substantially between species groups. Validation of models with testing data displayed limited predictive abilities of both model iterations. The most significant climate predictors for floodwater Aedes, the dominant and operationally influential species group in the county, were either total precipitation or frequency of precipitation events (number of "wet" days) at two to four weeks before trap collection week. Challenges hindering the construction of threshold models were discussed. Insights gained from these models provide initial feedback for streamlining the AMCD mosquito control program and analytical recommendations for future modelling efforts of interested mosquito control programs, in addition to generalized guidance for deployed armed forces personnel with needs of mosquito control but lacking active surveillance programs.

Mapping specific groundwater nitrate concentrations from spatial data using machine learning: A case study of chongqing, China

Groundwater resources is not only important essential water resources but also imperative connectors within the intricate framework of the ecological environment. High nitrate concentrations in groundwater can exerting adverse impacts on human health. It is imperative to accurately delineate the distribution characteristics of groundwater nitrate concentrations. Four different machine learning models (Gradient Boosting Regression (GB), Random Forest Regression (RF), Extreme Gradient Boosting Regression (XG) and Adaptive Boosting Regression (AD)) which combine spatial environmental data and different radius contributing area was developed to predict the distribution of nitrate concentration in groundwater. The models use 595 groundwater samples and included topography, remote sensing, hydrogeological and hydrological, climate, nitrate input, and socio-economic predictor. Gradient Boosting Regression model outperforms the other models (R2 = 0.627, MAE = 0.529, RMSE = 0.705, PICP = 0.924 for test dataset) under 500 m radius contributing area. A high-resolution (1 km) groundwater nitrate concentration distribution map reveal in the majority of the study area, groundwater nitrate concentrations are below 1 mg/L and high nitrate concentration (>10 mg/L) proportion in southeast, northeast and central main urban area karst valley regions is 1.89%, 0.91%, and 0.38% respectively. In study area, hydrogeological conditions, soil parameters, nitrogen input factors, and percentage of arable land are among the most influential explanatory factors. This work, serving as the inaugural application of utilizing effective spatial methods for predicting groundwater nitrate concentrations in Chongqing city, furnish decision-making support for the prevention and control of groundwater pollution, particularly in areas primarily dependent on groundwater for water supply and holds profound significance as a milestone achievement.

Global patterns of tree wood density.

Wood density is a fundamental property related to tree biomechanics and hydraulic function while playing a crucial role in assessing vegetation carbon stocks by linking volumetric retrieval and a mass estimate. This study provides a high-resolution map of the global distribution of tree wood density at the 0.01° (~1 km) spatial resolution, derived from four decision trees machine learning models using a global database of 28,822 tree-level wood density measurements. An ensemble of four top-performing models combined with eight cross-validation strategies shows great consistency, providing wood density patterns with pronounced spatial heterogeneity. The global pattern shows lower wood density values in northern and northwestern Europe, Canadian forest regions and slightly higher values in Siberia forests, western United States, and southern China. In contrast, tropical regions, especially wet tropical areas, exhibit high wood density. Climatic predictors explain 49%-63% of spatial variations, followed by vegetation characteristics (25%-31%) and edaphic properties (11%-16%). Notably, leaf type (evergreen vs. deciduous) and leaf habit type (broadleaved vs. needleleaved) are the most dominant individual features among all selected predictive covariates. Wood density tends to be higher for angiosperm broadleaf trees compared to gymnosperm needleleaf trees, particularly for evergreen species. The distributions of wood density categorized by leaf types and leaf habit types have good agreement with the features observed in wood density measurements. This global map quantifying wood density distribution can help improve accurate predictions of forest carbon stocks, providing deeper insights into ecosystem functioning and carbon cycling such as forest vulnerability to hydraulic and thermal stresses in the context of future climate change.

Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic.

The Arctic ecosystems and their species are exposed to amplified climate warming and, in some regions, to rapidly developing economic activities. This study assesses, models, and maps the geographic patterns of community-level plant species richness in the Western Siberian Arctic and estimates the relative impact of environmental and anthropogenic factors driving these patterns. With our study, we aim at contributing toward conservation efforts for Arctic plant diversity in the Western Siberian Arctic. Western Siberian Arctic, Russia. We investigated the relative importance of environmental and anthropogenic predictors of community-level plant species richness in the Western Siberian Arctic using macroecological models trained with an extensive geobotanical dataset. We included vascular plants, mosses and lichens in our analysis, as non-vascular plants substantially contribute to species richness and ecosystem functions in the Arctic. We found that the mean community-level plant species richness in this vast Arctic region does not decrease with increasing latitude. Instead, we identified an increase in species richness from South-West to North-East, which can be well explained by environmental factors. We found that paleoclimatic factors exhibit higher explained deviance compared to contemporary climate predictors, potentially indicating a lasting impact of ancient climate on tundra plant species richness. We also show that the existing protected areas cover only a small fraction of the regions with highest species richness. Our results reveal complex spatial patterns of community-level species richness in the Western Siberian Arctic. We show that climatic factors such as temperature (including paleotemperature) and precipitation are the main drivers of plant species richness in this area, and the role of relief is clearly secondary. We suggest that while community-level plant species richness is mostly driven by environmental factors, an improved spatial sampling will be needed to robustly and more precisely assess the impact of human activities on community-level species richness patterns. Our approach and results can be used to design conservation strategies and to investigate drivers of plant species richness in other arctic regions.

Biogeographical patterns of species richness in stream diatoms from southwestern South America.

The latitudinal diversity gradient (LDG) hypothesis has been validated for many taxon groups, but so far, stream diatoms have not conformed to this pattern. Research on diatoms that includes data from South America is lacking, and our study aims to address this knowledge gap. Previous studies have successfully explained stream diatom species richness by considering niche dimensionality of physicochemical variables. Moreover, in southwestern South America, the observed biogeographical pattern differs from LDG and has been shown to be determined by historical factors. We used a dataset comprising 373 records of stream diatom communities located between 35° S and 52° S latitude, southwestern South America. The dataset included physicochemical river water variables, climate data, and ice sheet cover from the Last Glacial Maximum. We explored geographical patterns of diatom species richness and evaluated 12 different causal mechanisms, including climate-related theories, physicochemical and climatical exploratory analyses, historical factors, and niche dimensionality. A metacommunity analysis was conducted to evaluate the possible nested structure due to historical factors. We observed an increase in diatom species richness from south to north. Models containing both physicochemical and climatic predictors explained the highest proportion of variation in the data. Silica, which was correlated with latitude, and flow velocity, which did not show any spatial pattern, were the most important predictors. Historical factors and nested structure did not play any role. Contrary to what has been reported in the literature, we found no support for climate-related explanations of species richness. Instead, theories related to niche dimensionality and local factors provided better explanations, consistent with previous related research. We suggest that the increase in diatom richness in the north of our study region is due to a higher nutrient supply in these rivers, rather than a due to larger species pool in the area.

Ecological niche modelling of Indigofera oblongifolia (Forssk.): a global machine learning assessment using climatic and non-climatic predictors

Climate change and other extinction facilitators have caused significant shifts in the distribution patterns of many species during the past few decades. Restoring and protecting lesser-known species may be more challenging without adequate biogeographical information. To address this knowledge gap, the current study set out to determine the global spatial distribution patterns of Indigofera oblongifolia (Forssk) a relatively lesser-known leguminous species. This was accomplished by utilizing three distinct bioclimatic temporal frames (current, 2050, and 2070) and four greenhouse gas scenarios (RCPs 2.6, 4.5, 6.0, and 8.5), in addition to non-climatic predictors such as global livestock population, human modification of terrestrial ecosystems, and global fertilizers application (nitrogen and phosphorus). Furthermore, we evaluate the degree of indigenousness using the geographical area, habitat suitability categories, and number of polygons. This research reveals that climatic predictors outperform non-climatic predictors in terms of improving model quality. Precipitation Seasonality is one of the most important factors influencing this species' optimum habitat suitability up to 150 mm for the current, 2050 RCP 8.5 and 2070-RCPs 2.6, 4.5, and 8.5. Our ellipsoid niche modelling extends the range of precipitation during the wettest quarter and maximum temperature during the warmest month to 637 mm and 26.5–31.80 degrees Celsius, respectively. India has a higher indigenous score in the optimal class than the African region. This findings suggest that the species in question tends to occupy contiguous regions in Africa, while in India, it is dispersed into several smaller meta-populations.

Sampling completeness changes perceptions of continental scale climate–species richness relationships in odonates

AbstractAimInsects are one of the least studied taxa, with most species lacking basic ecological and biogeographical information. This problem is particularly acute in the tropics, where low sampling effort hampers accurate estimates of species richness at scale and potentially confounds efforts to identify the drivers of biogeographical gradients. Here, we evaluate the quality of the data on the distribution and diversity of odonate species in the Neotropics, while also examining the influence of sampling completeness on climate–richness relationships using a comprehensive database of odonates.LocationThe Neotropics.TaxonOdonata.MethodsUsing 56,535 records collected from 1970 to 2021, we assess whether climate–species richness models vary under different scenarios of survey completeness.ResultsOur survey compilation revealed that most Neotropical diversity of Odonata likely remains unknown. Only 1% of the one‐degree cells covering the Neotropics held reliable information on odonate species richness, with particularly severe gaps in the Caribbean, Central America, northeastern Brazil and northern Chile. Temperature, precipitation and potential evapotranspiration exert consistent effects on Odonata richness across the entire Neotropics, regardless the level of survey completeness. Whereas seasonality‐related variables are less important predictors of species richness at the biogeographical scale.Main ConclusionsBy highlighting areas where inventories are more reliable and identifying regions that require increased data collection efforts and mobilization, our assessment offers a roadmap for improving the reliability of odonate inventories in the Neotropics. Furthermore, our findings underscore the importance of accounting for varying levels of survey completeness in macroecological models to reveal robust climate–species richness relationships. Simultaneously, they highlight strong climatic predictors of species richness, irrespective of survey effort intensity. These predictors provide a solid foundation for modelling and predicting odonate species richness in the Neotropics.

Potential impact of future climate change on grassland cover in Burkina Faso.

The objective of this study was to analyse the potential impact of future climate change on grassland cover in Burkina Faso. MODIS NDVI 250m time series were used to monitor changes in grassland over 2000-2022. The random forest regression (RFR) model was fit by regressing reference data of grassland cover against current climatic and other environmental predictors to predict the current grassland cover map (2022). Projected climate model data of CMIP6 used under SSP 126 and SSP 585 scenarios were integrated into the fit RFR model to predict future change. The results revealed that grassland areas were largely dominated by non-significant productivity change (55%) during 2000-2022. In this period, grassland area knew more increased productivity (35%) than decrease (10%). Burkina Faso is predicted to face more decreased areas of grassland cover than increase by 2061-2080 under SSP 126 and SSP 585 scenarios. The findings of this study can help to set up appropriate adaptation measures to combat climate change in Burkina Faso.

A global assessment of environmental and climate influences on wetland macroinvertebrate community structure and function

AbstractEstimating organisms' responses to environmental variables and taxon associations across broad spatial scales is vital for predicting their responses to climate change. Macroinvertebrates play a major role in wetland processes, but studies simultaneously exploring both community structure and community trait responses to environmental gradients are still lacking. We compiled a global dataset (six continents) from 756 depressional wetlands, including the occurrence of 96 macroinvertebrate families, their phylogenetic tree, and 19 biological traits. Using Bayesian hierarchical joint species distribution models (JSDMs), we estimated macroinvertebrate associations and compared the influences of local and climatic predictors on both individual macroinvertebrate families and their traits. While macroinvertebrate families were mainly related to broad‐scale factors (maximum temperature and precipitation seasonality), macroinvertebrate traits were strongly related to local wetland hydroperiod. Interestingly, macroinvertebrate families and traits both showed positive and negative associations to the same environmental variables. As expected, many macroinvertebrate family occurrences were positively associated with temperature, but a few showed the opposite pattern and were found in cooler or montane regions. We also found that wetland macroinvertebrate communities would likely be affected by changing climates through alterations in traits related to precipitation seasonality, temperature seasonality, and wetland area. Temperature increases may negatively affect collector and shredder functional groups. A decrease in precipitation could lead to reductions in wetland area benefiting drought‐tolerant macroinvertebrates, but it may negatively affect macroinvertebrates lacking those adaptations. Wetland processes may be compromised through broad‐scale environmental changes altering macroinvertebrate family distributions and local hydroperiod shifts altering organism traits. Our complementary family‐based and trait‐based approaches elucidate the complex effects that climate change may produce on wetland ecosystems.

Drivers of infection with Toxoplasma gondii genotype type II in Eurasian red squirrels (Sciurus vulgaris)

BackgroundIn September 2014, there was sudden upsurge in the number of Eurasian red squirrels (Sciurus vulgaris) found dead in the Netherlands. High infection levels with the parasite Toxoplasma gondii were demonstrated, but it was unclear what had caused this increase in cases of fatal toxoplasmosis. In the present study, we aimed to gain more knowledge on the pathology and prevalence of T. gondii infections in Eurasian red squirrels in the Netherlands, on the T. gondii genotypes present, and on the determinants of the spatiotemporal variability in these T. gondii infections. The presence of the closely related parasite Hammondia hammondi was also determined.MethodsEurasian red squirrels that were found dead in the wild or that had died in wildlife rescue centres in the Netherlands over a period of seven years (2014–2020) were examined. Quantitative real-time polymerase chain reaction was conducted to analyse tissue samples for the presence of T. gondii and H. hammondi DNA. Toxoplasma gondii-positive samples were subjected to microsatellite typing and cluster analysis. A mixed logistic regression was used to identify climatic and other environmental predictors of T. gondii infection in the squirrels.ResultsA total of 178 squirrels were examined (49/178 T. gondii positive, 5/178 H. hammondi positive). Inflammation of multiple organs was the cause of death in 29 squirrels, of which 24 were also T. gondii polymerase chain reaction positive. Toxoplasma gondii infection was positively associated with pneumonia and hepatitis. Microsatellite typing revealed only T. gondii type II alleles. Toxoplasma gondii infection rates showed a positive correlation with the number of days of heavy rainfall in the previous 12 months. Conversely, they showed a negative association with the number of hot days within the 2-week period preceding the sampling date, as well as with the percentage of deciduous forest cover at the sampling site.ConclusionsToxoplasma gondii infection in the squirrels appeared to pose a significant risk of acute mortality. The T. gondii genotype detected in this study is commonly found across Europe. The reasons for the unusually high infection rates and severe symptoms of these squirrels from the Netherlands remain unclear. The prevalence of T. gondii in the deceased squirrels was linked to specific environmental factors. However, whether the increase in the number of dead squirrels indicated a higher environmental contamination with T. gondii oocysts has yet to be established.Graphical

Assessment of Climate Change Impact on Precipitation Using Machine Learning Based Statistical Downscaling Method

Future predictions of precipitation are highly important for effective water resources management. The Global circulation models (GCMs) are commonly used to make such predictions. In this paper, the effect of climate change on precipitation was investigated for Damaturu station located in Yobe state, Nigeria from 2050-2080. For this purpose, the BNU-ESM GCMs under the emission scenario RCP 4.5 was used to downscale observed precipitation data via Artificial Neural Network (ANN). Various climatic predictors were considered and ranked according to their impact on precipitation using the mutual information (MI) method. A total of 5 ANN models were subsequently developed using different combinations of predictors as inputs to downscale the precipitation data. The Determination Coefficient (DC) and Root Mean Square Error (RMSE) performance indicators were then employed. M1 which used a combination of top 8 ranked predictors was found to have the best performance in both downscaling and projection phases. The final results from M1 showed that, over the specified period, the Damaturu region will generally experience a decrease in precipitation, which will be more prevalent in months that experience the most precipitation with the most decrease of 20% in monthly precipitation sum occurring during the wettest month of August, towards the end of the 21st century.

Projected climate and canopy change lead to thermophilization and homogenization of forest floor vegetation in a hotspot of plant species richness.

Mountain forests are plant diversity hotspots, but changing climate and increasing forest disturbances will likely lead to far-reaching plant community change. Projecting future change, however, is challenging for forest understory plants, which respond to forest structure and composition as well as climate. Here, we jointly assessed the effects of both climate and forest change, including wind and bark beetle disturbances, using the process-based simulation model iLand in a protected landscape in the northern Alps (Berchtesgaden National Park, Germany), asking: (1) How do understory plant communities respond to 21st-century change in a topographically complex mountain landscape, representing a hotspot of plant species richness? (2) How important are climatic changes (i.e., direct climate effects) versus forest structure and composition changes (i.e., indirect climate effects and recovery from past land use) in driving understory responses at landscape scales? Stacked individual species distribution models fit with climate, forest, and soil predictors (248 species currently present in the landscape, derived from 150 field plots stratified by elevation and forest development, overall area under the receiving operator characteristic curve = 0.86) were driven with projected climate (RCP4.5 and RCP8.5) and modeled forest variables to predict plant community change. Nearly all species persisted in the landscape in 2050, but on average 8% of the species pool was lost by the end of the century. By 2100, landscape mean species richness and understory cover declined (-13% and -8%, respectively), warm-adapted species increasingly dominated plant communities (i.e., thermophilization, +12%), and plot-level turnover was high (62%). Subalpine forests experienced the greatest richness declines (-16%), most thermophilization (+17%), and highest turnover (67%), resulting in plant community homogenization across elevation zones. Climate rather than forest change was the dominant driver of understory responses. The magnitude of unabated 21st-century change is likely to erode plant diversity in a species richness hotspot, calling for stronger conservation and climate mitigation efforts.

Predicting Streamflow Duration From Crowd‐Sourced Flow Observations

AbstractStreamflow duration is important for aquatic ecosystems and assigning stream protection status. This study predicts streamflow duration, represented as the fraction of time with flow each year, using a combination of sensor data and crowd‐sourced visual observations for a study area in northern Colorado, USA. We used 11 stream stage sensors and 177 visual monitoring points to examine how frequently streams should be sampled to compute flow fractions accurately. This showed that the number of visual observations needed to compute accurate flow fractions increases with decreasing flow duration. We then developed random forest models to predict mean annual flow fractions using climate, topographic, and land cover predictors and found that snow persistence, summer precipitation, and drainage area were important predictors. Model performance was best when using sites with ≥10 visual observations. Our model predicts that almost all (98%) of streams in the study region are non‐perennial, about 10% more than the amount of non‐perennial streams in the National Hydrography Dataset. Stream type maps are sensitive to the time period of data collection and to thresholds used to represent perennial versus non‐perennial flow. To improve maps of non‐perennial streams, we recommend moving beyond categorical classification of streams to a continuous variable like flow fraction. These efforts can be best supported with frequent observations in time that span streams with a wide range of flow fractions and drainage area attributes.

CLIMATOGENIC INFLUENCE AND PREDICTION OF SEASONAL RHYTHM CHANGES IN THE MAIN FOREST-FORMING SPECIES OF THE NORTHEASTERN CARPATHIANS

The article is devoted to the study of the phenological phenomena of the main forest-forming species (European spruce - Picea abies, white fir - Abies alba and forest beech - Fagus sylvatica) within the temperate climate zone (600-800 m a.s.l.) on the territory of the Carpathian National Nature Park for the period 2000-2022. The study employed a comprehensive set of field, instrumental, laboratory, analytical, and mathematical-statistical research methods, along with certain approaches applied in phenology and meteorology. According to the results of the statistical analysis, a significant influence of precipitation in March on the beginning of bud swelling (Picea abies and Fagus sylvatica species), the beginning of flowering trees (Picea abies, Abies alba species) and the onset of fruit ripening (Picea abies, Fagus sylvatica species) was revealed. and the beginning of seed dispersal (Picea abies, Abies alba species). It should be noted that the minimum air temperatures in July significantly affect phenological events that begin and continue at this time and later, in particular, the beginning of fruit ripening (Abies alba, Fagus sylvatica species) and the onset of seeds dispersal (in all species). Strong and moderate correlations between January-February maximum temperatures and various phenological events (e.g., in Picea abies, dates of shoot growth and flowering initiation; in Abies alba, dates of flowering initiation and seed ripening initiation; in Fagus sylvatica, dates of bud swelling and flowering initiation) suggest a notable impact of underground water, originating from meltwater during this period, on plant seasonal rhythms. The most difficult for modeling was the phenomenon of the beginning of flowering of trees, the formation of which is influenced by all involved climate predictors - precipitation, minimum and maximum air temperatures. Based on the results of the research, the initial long-term forecasting of the response of local forest crops to probable climate changes was carried out; so, it was shown that even under the worst-case scenario (RCP 8.5), the dates of occurrence of all phenological phenomena of model tree species will be within the scope of the sample of actual data for the observation period of 2000-2022.

Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems.

A tenet of ecology is that temporal variability in ecological structure and processes tends to decrease with increasing spatial scales (from locales to regions) and levels of biological organization (from populations to communities). However, patterns in temporal variability across trophic levels and the mechanisms that produce them remain poorly understood. Here we analyzed the abundance time series of spatially structured communities (i.e., metacommunities) spanning basal resources to top predators from 355 freshwater sites across three continents. Specifically, we used a hierarchical partitioning method to disentangle the propagation of temporal variability in abundance across spatial scales and trophic levels. We then used structural equation modeling to determine if the strength and direction of relationships between temporal variability, synchrony, biodiversity, and environmental and spatial settings depended on trophic level and spatial scale. We found that temporal variability in abundance decreased from producers to tertiary consumers but did so mainly at the local scale. Species population synchrony within sites increased with trophic level, whereas synchrony among communities decreased. At the local scale, temporal variability in precipitation and species diversity were associated with population variability (linear partial coefficient, β = 0.23) and population synchrony (β = -0.39) similarly across trophic levels, respectively. At the regional scale, community synchrony was not related to climatic or spatial predictors, but the strength of relationships between metacommunity variability and community synchrony decreased systematically from top predators (β = 0.73) to secondary consumers (β = 0.54), to primary consumers (β = 0.30) to producers (β = 0). Our results suggest that mobile predators may often stabilize metacommunities by buffering variability that originates at the base of food webs. This finding illustrates that the trophic structure of metacommunities, which integrates variation in organismal body size and its correlates, should be considered when investigating ecological stability in natural systems. More broadly, our work advances the notion that temporal stability is an emergent property of ecosystems that may be threatened in complex ways by biodiversity loss and habitat fragmentation.

Distribution and treatment needs of soil-transmitted helminthiasis in Bangladesh: A Bayesian geostatistical analysis of 2017-2020 national survey data.

In Bangladesh, preventive chemotherapy targeting soil-transmitted helminth (STH) infections in school-age children has been implemented since 2008. To evaluate the success of this strategy, surveys were conducted between 2017 and 2020 in 10 out of 64 districts. We estimate the geographic distribution of STH infections by species at high spatial resolution, identify risk factors, and estimate treatment needs at different population subgroups. Bayesian geostatistical models were fitted to prevalence data of each STH species. Climatic, environmental, and socioeconomic predictors were extracted from satellite images, open-access, model-based databases, and demographic household surveys, and used to predict the prevalence of infection over a gridded surface at 1 x 1 km spatial resolution across the country, via Bayesian kriging. These estimates were combined with gridded population data to estimate the number of required treatments for different risk groups. The population-adjusted prevalence of Ascaris lumbricoides, Trichuris trichiura, and hookworm across all ages is estimated at 9.9% (95% Bayesian credible interval: 8.0-13.0%), 4.3% (3.0-7.3%), and 0.6% (0.4-0.9%), respectively. There were 24 out of 64 districts with an estimated population-adjusted STH infection prevalence above 20%. The proportion of households with improved sanitation showed a statistically important, protective association for both, A. lumbricoides and T. trichiura prevalence. Precipitation in the driest month of the year was negatively associated with A. lumbricoides prevalence. High organic carbon concentration in the soil's fine earth fraction was related to a high hookworm prevalence. Furthermore, we estimated that 30.5 (27.2; 36.0) million dosages of anthelmintic treatments for school-age children were required per year in Bangladesh. For each of the STH species, the prevalence was reduced by at least 80% since treatment was scaled up more than a decade ago. The current number of deworming dosages could be reduced by up to 61% if the treatment strategy was adapted to the local prevalence.

New insight into drivers of mammalian litter size from individual‐level traits

The digitization and open availability of life history traits measured directly from individuals provide a key means of linking organismal function to environmental and ecological contexts at fine resolution. These linkages play a critical role in understanding trait‐mediated response to global change, with particular need to resolve them for taxa that are secretive and hard to monitor, like most mammals. In this study, we use digitized museum specimen and census data to document how climate and body size each shape a key life history trait – litter size – in 39 small mammals across North America. We employ mixed models to test associations between litter size, climate and body size, with a focus on joint estimation of inter‐ and intraspecific trends. Among species, no single climate predictor explained a large amount of litter size variation. Instead, interactions between temperature‐related (continentality, solar irradiation) and moisture‐related (annual precipitation, relative humidity) indices, along with body size, exert a stronger influence on litter size. We observed maximal litter sizes for species inhabiting seasonal or xeric regions under conditions of increased moisture availability, or conversely, mesic or aseasonal regions under conditions of reduced moisture availability. These patterns are consistent with primary productivity as a mechanistic driver of litter size. At the intraspecific level, litter size responds to continentality and temperature‐related indices experienced by populations, but is most strongly shaped by body size of individual females. We also find evidence of phylogenetic covariation in these intraspecific trends. Our study demonstrates how life history traits assembled from individual‐level biodiversity records improve precision and granularity of ecological studies, help to parse among‐ and within‐species trends, and foster improved understanding of tradeoffs between energetic supply and demand (e.g. reproduction) in wild mammals.

Evaluation of the Spatial Distribution of Predictors of Fire Regimes in China from 2003 to 2016

Wildfire has extensive and profound impacts on forest structure and function. Therefore, it is important to study the spatial and temporal patterns of forest fire regimes and their drivers in order to better understand the feedbacks between climate change, fire disturbance, and forest ecosystems. Based on the Global Fire Atlas dataset, three forest fire regime components (fire occurrence density, burned rate, and median fire size) were extracted for China from 2003 to 2016. Three statistical models (Boosted Regression Tree, Random Forest, and Support Vector Machine) were used to systematically analyze the relationships between patterns of forest fire disturbance and climate, human activities, vegetation, and topography in China, as well as their spatial heterogeneity in different climatic regions. The results indicate that the spatial distribution of forest fires is heterogeneous, and different forest fire regime components are predicted by different factors. At the national level, the distribution of forest fire regimes mainly corresponds to climatic factors, although the relationship between median fire size and predictors is obscure. At the scale of each ecoregion, the main climate predictors of forest fire occurrence density and burned rate change from temperature in the north to temperature and precipitation in the south. Median fire size varies with elevation and temperature in the south. These results demonstrate that the spatial heterogeneity of predictors and scaling effects must be fully considered in the study of forest fire disturbance.

Global distribution modelling of a conspicuous Gondwanian soil protist reveals latitudinal dispersal limitation and range contraction in response to climate warming

AbstractAimThe diversity and distribution of soil microorganisms and their potential for long‐distance dispersal (LDD) are poorly documented, making the threats posed by climate change difficult to assess. If microorganisms do not disperse globally, regional endemism may develop and extinction may occur due to environmental changes. Here, we addressed this question using the testate amoeba Apodera vas, a morphologically conspicuous model soil microorganism in microbial biogeography, commonly found in peatlands and forests mainly of former Gondwana. We first documented its distribution. We next assessed whether its distribution could be explained by dispersal (i.e. matching its climatic niche) or vicariance (i.e. palaeogeography), based on the magnitude of potential range expansions or contractions in response to past and on‐going climatic changes. Last, we wanted to assess the likelihood of cryptic diversity and its potential threat from climate and land‐use changes (e.g. due to limited LDD).LocationDocumented records: Southern Hemisphere and intertropical zone; modelling: Global.MethodsWe first built an updated global distribution map of A. vas using 401 validated georeferenced records. We next used these data to develop a climatic niche model to predict its past (LGM, i.e. 21 ± 3 ka BP; PMIP3 IPSL‐CM5A‐LR), present and future (IPSL‐CMP6A‐LR predictions for 2071–2100, SSP3 and 5) potential distributions in responses to climate, by relating the species occurrences to climatic and topographic predictors. We then used these predictions to test our hypotheses (dispersal/vicariance, cryptic diversity, future threat from LDD limitation).ResultsOur models show that favourable climatic conditions for A. vas currently exist in the British Isles, an especially well‐studied region for testate amoebae where this species has never been found. This demonstrates a lack of interhemispheric LDD, congruent with the palaeogeography (vicariance) hypothesis. Longitudinal LDD is, however, confirmed by the presence of A. vas in isolated and geologically young peri‐Antarctic islands. Potential distribution maps for past, current and future climates show favourable climatic conditions existing on parts of all southern continents, with shifts to higher land from LGM to current in the tropics and a strong range contraction from current to future (global warming IPSL‐CM6A‐LR scenario for 2071–2100, SSP3.70 and SSP5.85) with favourable conditions developing on the Antarctic Peninsula.Main ConclusionsThis study illustrates the value of climate niche models for research on microbial diversity and biogeography, along with exploring the role played by historical factors and dispersal limitation in shaping microbial biogeography. We assess the discrepancy between latitudinal and longitudinal LDD for A. vas, which is possibly due to contrast in wind patterns and/or likelihood of transport by birds. Our models also suggest that climate change may lead to regional extinction of terrestrial microscopic organisms, thus illustrating the pertinence of including microorganisms in biodiversity conservation research and actions.

Tweedie models for Malaysia rainfall simulations with seasonal variabilities

Abstract This study aims to evaluate the suitability of the Tweedie generalised linear model for characterising monthly rainfall patterns across 18 meteorological stations in Peninsular Malaysia. It incorporates harmonic functions consisting of sine and cosine functions as seasonal predictors and El Niño Southern Oscillation (ENSO) indices as climatic predictors. Results indicate that three harmonic functions are essential to accurately portray rainfall dynamics in the southwestern and northwestern regions, while two suffice for the inland and western regions. However, incorporating four harmonic functions is the most optimal representation of the eastern region. An additional 1-month lag in ENSO indices is introduced to the optimal seasonal predictor model. Based on the findings, the southern oscillation index notably impacts monthly rainfall significantly in eastern and inland areas, while meteorological stations in the western and northwestern areas fit better with the multivariate ENSO index. Strikingly, no substantial impact of climate predictors is observed on the monthly rainfall within the southwestern region. Thus, the influence of climate indices is very much influenced by the geographical locations of the regions. Importantly, generating simulated data through the Tweedie model contributes to a more accurate representation of the statistical properties inherent in rainfall analysis.