The impact of climatic variables on the population dynamics of the main malaria vector, Anopheles stephensi Liston(Diptera: Culicidae), in southern Iran

Objective:To determine the significance of temperature,rainfall and humidity in the seasonal abundance of Anopheles stephensi in southern Iran.Methods:Data on the monthly abundance of Anopheles stephensi larvae and adults were gathered from earlier studies conducted between 2002 and 2019 in malaria prone areas of southeastern Iran.Climatic data for the studied counties were obtained from climatology stations.Generalized estimating equations method was used for cluster correlation of data for each study site in different years.Results:A significant relationship was found between monthly density of adult and larvae of Anopheles stephensi and precipitation,max temperature and mean temperature,both with simple and multiple generalized estimating equations analysis(P<0.05).But when analysis was done with one month lag,only relationship between monthly density of adults and larvae of Anopheles stephensi and max temperature was significant(P<0.05).Conclusions:This study provides a basis for developing multivariate time series models,which can be used to develop improved appropriate epidemic prediction systems for these areas.Long-term entomological study in the studied sites by expert teams is recommended to compare the abundance of malaria vectors in the different areas and their association with climatic variables.

On the Relationship between Climatic Variables and Pressure Systems over Saudi Arabia in the Winter Season

The synoptic circulation over Saudi Arabia is complicated and frequently governed by the effect of large-scale pressure systems. In this work, we used NCEP–NCAR global data to illustrate the relationship between climatic variables and the main pressure systems that affect the weather and climate of Saudi Arabia, and also to investigate the influence of these pressure systems on surface air temperature（SAT） and rainfall over the region in the winter season. It was found that there are two primary patterns of pressure that influence the weather and climate of Saudi Arabia. The first occurs in cases of a strengthening Subtropical High（Sub H）, a weakening Siberian High（Sib H）, a deepening of the Icelandic Low（Ice L）, or a weakening of the Sudanese Low（Sud L）. During this pattern, the Sub H combines with the Sib H and an obvious increase of sea level pressure（SLP） occurs over southern European, the Mediterranean, North Africa, and the Middle East. This belt of high pressure prevents interaction between midlatitude and extratropical systems, which leads to a decrease in the SAT,relative humidity（RH） and rainfall over Saudi Arabia. The second pattern occurs in association with a weakening of the Sub H, a strengthening of the Sib H, a weakening of the Ice L, or a deepening of the Sud L. The pattern arising in this case leads to an interaction between two different air masses： the first（cold moist） air mass is associated with the Mediterranean depression travelling from west to east, while the second（warm moist） air mass is associated with the northward oscillation of the Sud L and its inverted V-shape trough. The interaction between these two air masses increases the SAT, RH and the probability of rainfall over Saudi Arabia, especially over the northwest and northeast regions.

Intertidal Biodiversity and Their Response to Climatic Variables, Temperature and pH—What We Know

As per the Essential Climate Variables (ESV) of World Meterological Organisation (WMO), the physical, chemical and biological variables critically contribute to the earth’s climate. Among them, the variables such as temperature and pH in the marine environment may affect seriously and in turn it has an impact on the biota, especially in the intertidal environment, where it has brunt force. According to United Nations Framework Convention on Climate Change (UNFCCC), the datasets should provide the empirical evidence needed to predict the climate change and evoluate the mitigation and adaptation measures. Under this context, a review was carried out to know what extent marine scientists understand this factor and what level the biodiversity was evoluated and its impact was analysed in this article. Based on the existing literature review, it was understood that only a few groups that also only few species from these groups were studied in this aspect. The remaining groups and their species and their basic trophic were not evolved in this aspect. So, the marine scientific community, environmentalist and policy makers should take stock on this aspect and give thrust on this study.

Responses of Annual Variability of Vegetation NPP to Climate Variables Using Satellite Techniques in Gadarif State, Sudan

Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.

Agro-Climatic Risks Analysis in Climate Variability Context in Ségou Region

In the Sahel region, the population depends largely on rain-fed agriculture. In West Africa in particular, climate models turn out to be unable to capture some basic features of present-day climate variability. This study proposes a contribution to the analysis of the evolution of agro-climatic risks in the context of climate variability. Some statistical tests are used on the main variables of the rainy season to determine the trends and the variabilities described by the data series. Thus, the paper provides a statistical modeling of the different agro-climatic risks while the seasonal variability of agro-climatic parameters was analyzed as well as their inter annual variability. The study identifies the probability distributions of agroclimatic risks and the characterization of the rainy season was clarified.

Seasonal Dynamic of the Fall Armyworm, Spodoptera frugiperda (J.E Smith, 1797) (Lepidoptera: Noctuidae) on Maize Crop in the Sub-Sudanese Zone of Côte d’Ivoire

In Côte d’Ivoire, maize (Zea mays L) is the second most cultivated cereal after rice. Since the first report of Spodoptera frugiperda in Côte d’Ivoire, maize production in the northern regions has been affected resulting in maize production losses. This study aims to study the seasonal dynamic of Spodoptera frugiperda in maize fields in the sub-Sudanese zone, main zone of maize cultivation in Côte d’Ivoire. The study was done using pheromone trap lures. The results revealed a variation in the moth population at various growth stages during rainy and dry seasons. Notably, the highest numbers of moths were consistently trapped during the whorl stage with counts ranging from 131 ± 35.7 during the rainy season to 70.6 ± 15.01 in the dry season. The lowest numbers of moths were observed during pod maturation, with counts ranging from 30.3 ± 13.05 during the rainy season to 11.7 ± 3.05 in the dry season. Between the 7th and 21st days after sowing, the count of moths displayed a consistent upward trajectory, reaching 188 moths during the rainy season. The damages were particularly observed at whorl stage. The relationship between the numbers of moths and some climatic variables revealed a negative correlation between moths numbers and rainfall (r= −0.44) and relative humidity (r= −0.684). In contrast, there were positive relationships with temperature (r = 0.16), highlighting the significant impact of temperature changes on moth population dynamics. The research highlights the need for integrated pest management strategies that consider climatic factors and growth stages of maize to mitigate the impact of this insect pest on maize.

Arctic Sea Ice Variations in the First Half of the 20th Century:A New Reconstruction Based on Hydrometeorological Data

The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

Trends in major and minor meteorological variables and their influence on reference evapotranspiration for mid Himalayan region at east Sikkim, India

Abstract： Estimation of evapotranspiration （ET） for mountain ecosystem is of absolute importance since it serves as an important component in balancing the hydrologic cycle. The present study evaluates the performance of original and location specific calibrated Hargreaves equation （HARG） with the estimates of Food and Agricultural Organization （FAO） Penman Monteith （PM） method for higher altitudes in East Sikkim, India. The results show that the uncalibrated HARG model underestimates ET0 by 0.35 mm day^-1 whereas the results are significantly improved by regional calibration of the model. In addition, this paper also presents the variability in the trajectory associated with the climatic variables with the changing climate in the study site. Non- parametric Mann-Kendall （MK） test was used to investigate and understand the mean monthly trend of eight climatic parameters including reference evapotranspiration （ET0） for the period of 1985 - 2009. Trend of ET0 was estimated for the calculations done by FAO PM equation. The outcomes of the trend analysis show significant increasing （p ≤ 0.05） trend represented by higher Z-values, through MK test, for net radiation （Rn）, maximum temperature （Tmax） and minimum temperature （Train）, especially in the first months of the year. Whereas, significant （0.01 ≥ p ≤0.05） decreasing trend in vapor pressure deficit （VPD） and precipitation （P） is observed throughout the year. Declining trend in sunshine duration, VPD and ET0 is found in spring （March - May） and monsoon （June - November） season. The result displays significant （0.01≤ p ≤0.05） decreasing ET0 trend between （June - December） except in July, exhibiting the positive relation with VPD followed by sunshine duration at the station. Overall, the study emphasizes the importance of trend analysis of ET0 and other climatic variables for efficient planning and managing the agricultural practices, in identifying the changes in the meteorological parameters and to accurately assess the hydrologic water balance of the hilly regions.

Responses of runoff to changes in climate and human activities in the Liuhe River Basin, China

Since the 1950s,numerous soil and water conservation measures have been implemented to control severe soil erosion in the Liuhe River Basin(LRB),China.While these measures have protected the upstream soil and water ecological environment,they have led to a sharp reduction in the downstream flow and the deterioration of the river ecological environment.Therefore,it is important to evaluate the impact of soil and water conservation measures on hydrological processes to assess long-term runoff changes.Using the Soil and Water Assessment Tool(SWAT)models and sensitivity analyses based on the Budyko hypothesis,this study quantitatively evaluated the effects of climate change,direct water withdrawal,and soil and water conservation measures on runoff in the LRB during different periods,including different responses to runoff discharge,hydrological regime,and flood processes.The runoff series were divided into a baseline period(1956-1969)and two altered periods,i.e.,period 1(1970-1999)and period 2(2000-2020).Human activities were the main cause of the decrease in runoff during the altered periods,contributing 86.03%(-29.61 mm),while the contribution of climate change was only 13.70%(-4.70 mm).The impact of climate change manifests as a decrease in flood volume caused by a reduction in precipitation during the flood season.Analysis of two flood cases indicated a 66.00%-84.00%reduction in basin runoff capacity due to soil and water conservation measures in the upstream area.Soil and water conservation measures reduced the peak flow and total flood volume in the upstream runoff area by 77.98%and 55.16%,respectively,even with nearly double the precipitation.The runoff coefficient in the reservoir area without soil and water conservation measures was 4.0 times that in the conservation area.These results contribute to the re-evaluation of soil and water conservation hydrological effects and provide important guidance for water resource planning and water conservation policy formulation in the LRB.

Improving Optimal Fingerprinting Methods Requires a Viewpoint beyond Statistical Science

While being successful in the detection and attribution of climate change,the optimal fingerprinting method(OFM)may have some limitations from a physics-and-dynamics-based viewpoint.Here,an analysis is made on the linearity,noninteraction,and stationary-variability assumptions adopted by OFM.It is suggested that furthering OFM needs a viewpoint beyond statistical science,and the method should be combined with theoretical tools in the dynamics and physics of the Earth system,so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.

Spatial distribution characteristics of climate-induced landslides in the Eastern Himalayas

Climate warming is constantly causing hydro-meteorological perturbations,especially in high-altitude mountainous regions,which lead to the occurrences of landslides.The impact of climatic variables(i.e.,precipitation and temperature)on the distribution of landslides in the eastern regions of the Himalayas is poorly understood.To address this,the current study analyzes the relationship between the spatial distribution of landslide characteristics and climatic variables from 2013 to 2021.Google Earth Engine(GEE)was employed to make landslide inventories using satellite data.The results show that 2163,6927,and 9601 landslides were heterogeneously distributed across the study area in 2013,2017,and 2021,respectively.The maximum annual temperature was positively correlated with the distribution of landslides,whereas precipitation was found to have a non-significant impact on the landslide distribution.Spatially,most of the landslides occurred in areas with maximum annual precipitation ranging from 800 to 1600 mm and maximum annual temperature above 15℃.However,in certain regions,earthquake disruptions marginally affected the occurrence of landslides.Landslides were highly distributed in areas with elevations ranging between 3000 and 5000 m above sea level,and many landslides occurred near the lower permafrost limit and close to glaciers.The latter indicates that temperature change-induced freeze-thaw action influences landslides in the region.Temperature changes have shown a positive correlation with the number of landslides within elevations,indicating that temperature affects their spatial distribution.Various climate projections suggest that the region will experience further warming,which will increase the likelihood of landslides in the future.Thus,it is crucial to enhance ground observation capabilities and climate datasets to effectively monitor and mitigate landslide risks.

Continual Reinforcement Learning for Intelligent Agricultural Management under Climate Changes

Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural production.Existing works with traditional Reinforcement Learning(RL)methods often falter under such extreme conditions.To address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management strategies.Leveraging the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions.By incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios.The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe droughts.Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased temperatures.This pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.

Analysing the Potential Impact of Climate Change on the Hydrological Regime of the Upper Benue River Basin (North Cameroon)

In this study, we analyse the climate variability in the Upper Benue basin and assess its potential impact on the hydrology regime under two different greenhouse gas emission scenarios. The hydrological regime of the basin is more vulnerable to climate variability, especially precipitation and temperature. Observed hydroclimatic data (1950-2015) was analysed using a statistical approach. The potential impact of future climate change on the hydrological regime is quantified using the GR2M model and two climate models: HadGEM2-ES and MIROC5 from CMIP5 under RCP 4.5 and RCP 8.5 greenhouse gas emission scenarios. The main result shows that precipitation varies significantly according to the geographical location and time in the Upper Benue basin. The trend analysis of climatic parameters shows a decrease in annual average precipitation across the study area at a rate of -0.568 mm/year which represents about 37 mm/year over the time 1950-2015 compared to the 1961-1990 reference period. An increase of 0.7°C in mean temperature and 14% of PET are also observed according to the same reference period. The two climate models predict a warming of the basin of about 2°C for both RCP 4.5 and 8.5 scenarios and an increase in precipitation between 1% and 10% between 2015 and 2100. Similarly, the average annual flow is projected to increase by about +2% to +10% in the future for both RCP 4.5 and 8.5 scenarios between 2015 and 2100. Therefore, it is primordial to develop adaptation and mitigation measures to manage efficiently the availability of water resources.

A Study on Reconstruction of Surface Wind Speed in China Due to Various Climate Variabilities

Using European Centre for Medium-Range Weather Forecasts Reanalysis V5(ERA5)reanalysis data,this study investigated the reconstruction effects of various climate variabilities on surface wind speed in China from 1979 to 2022.The results indicated that the reconstructed annual mean wind speed and the standard deviation of the annual mean wind speed,utilizing various climate variability indices,exhibited similar spatial modes to the reanalysis data,with spatial correlation coefficients of 0.99 and 0.94,respectively.In the reconstruction of six major wind power installed capacity provinces/autonomous regions in China,the effects were notably good for Hebei and Shanxi provinces,with the correlation coefficients for the interannual regional average wind speed time series being 0.65 and 0.64,respectively.The reconstruction effects of surface wind speed differed across seasons,with spring and summer reconstructions showing the highest correlation with reanalysis data.The correlation coefficients for all seasons across most regions in China ranged between 0.4 and 0.8.Among the reconstructed seasonal wind speeds for the six provinces/autonomous regions,Shanxi Province in spring exhibited the highest correlation with the reanalysis,with a coefficient of 0.61.The large-scale climate variability indices showed good reconstruction effects on the annual mean wind speed in China,and could explain the interannual variability trends of surface wind speed in most regions of China,particularly in the main wind energy provinces/autonomous regions.

Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India

Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth to groundwater level(DGWL)and the impacts of climatic(precipitation,maximum temperature,and minimum temperature)and anthropogenic(gross district product(GDP),population,and net irrigated area(NIA))variables on DGWL during 1994-2020.The study considered DGWL in 113 observation wells and piezometers located in arid western plains(Barmer and Jodhpur districts)and semi-arid eastern plains(Jaipur,Ajmer,Dausa,and Tonk districts)of Rajasthan State,India.Statistical methods were employed to examine the annual and seasonal patterns of DGWL,and the generalized additive model(GAM)was used to determine the impacts of climatic and anthropogenic variables on DGWL.During 1994-2020,except for Barmer District,where the mean annual DGWL was almost constant(around 26.50 m),all other districts exhibited increase in DGWL,with Ajmer District experiencing the most increase.The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL.Similarly,32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter;33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon;35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon;and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon.Interestingly,most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts.Furthermore,the GAM analysis revealed that climatic variables,such as precipitation,significantly affected DGWL in Barmer District,and DGWL in all other districts was influenced by anthropogenic variables,including GDP,NIA,and population.As a result,stringent regulations should be implemented to curb excessive groundwater extraction,manage agricultural water demand,initiate proactive aquifer recharge programs,and strengthen sustainable management in these water-scarce regions.

Multi-decadal Changes of the Impact of El Niño Events on Tibetan Plateau Summer Precipitation

Precipitation on the Tibetan Plateau(TP)has an important effect on the water supply and demand of the downstream population.Involving recent climate change,the multi-decadal variations of the impact of El Niño-Southern Oscillation(ENSO)events on regional climate were observed.In this work,the authors investigated the changes in summer precipitation over TP during 1950-2019.At the multi-decadal scale,the authors found that the inhabiting impact of El Niño events on the TP summer precipitation has strengthened since the late 1970s.The main factor contributing to this phenomenon is the significant amplification in the decadal amplitude of El Niño during 1978-2019 accompanied by a discernible escalation in the frequency of El Niño events.This phenomenon induces anomalous perturbations in sea surface temperatures(SST)within the tropical Indo-Pacific region,consequently weakening the atmospheric vapor transport from the western Pacific to the TP.Additionally,conspicuous anomalies in subsidence motion are observed longitudinally and latitudinally across the TP which significantly contributes to a curtailed supply of atmospheric moisture.These results bear profound implications for the multi-decadal prediction of the TP climate.

Analysis of Extreme Temperature Variability in Rwanda

The temperature is one of the most important factors in weather and climate forecasting.Studying its behaviour is crucial to understanding climate variability,which could vary spatially and temporally at local,regional,and global scales.Several recent studies on air temperature findings show that the Earth’s near surface air temperature increased between 0.6℃ and 0.8℃ throughout the twentieth century.Using temperature records from ten meteorological stations,this study examined climate variability in Rwanda from the 1930s to 2014.The air temperature data were collected from Meteo Rwanda.Before making the analysis,the authors used software,such as Excel 2007 and INSTAT to control the quality of the raw data.The analysis of maxima and minima indicated that the trends of maximum air temperature were positive and significant at height meteorological stations,whereas the trends for minimum air temperature were found to be at 10 meteorological stations.For all parameters analysed,Kigali Airport meteorological station indicated the higher significance of the trends.The majority of meteorological stations showed an increase in both hot days and nights,confirming Rwanda’s warming over time.The analysis of average seasonal air temperature showed almost similar trends even though not all were significant.This similarity in trends could be attributed to the fact that Rwanda’s short and long dry seasons coincide with rainy seasons.

Key Variables Explaining Soil Organic Carbon Content Variations in Croplands and Non-Croplands in Chinese Provinces

Soil organic carbon (SOC) plays an important role in global carbon cycles.Large spatial variations in SOC contents result in uncertain estimates of the SOC pool and its changes.In the present study,the key variables explaining the SOC contents of croplands (CPs) and non-croplands (NCPs) in Chinese provinces were investigated.Data on SOC and other soil properties (obtained from the Second National Soil Survey conducted in the late 1970s to the early 1990s),climate parameters,as well as the proportion of the CP to the total land area (Pcp) were used.SOC content variations within a province were larger than those among provinces.Soil clay and total phosphorus content,ratio of annual precipitation to mean temperature,as well as Pcp were able to explain 75% of the SOC content variations in whole soil samples.Soil pH,mean temperature during the growing season from May to October,and mean annual wind velocity were able to explain 63% of the SOC content variations in NCP soils.Compared with NCP soils,CP soils had lower SOC contents,with smaller variations within and among provinces and lower C/N ratios.Stepwise regression showed that the soil clay content was a unique factor significantly correlated with the SOC content of CP soils.However,this factor only explained 24% of the variations.This result suggested that variables related to human activities had greater effects on SOC content variations in CP soils than soil properties and climate parameters.Based on SOC contents directly averaged from soil samples and estimated by regression equations,the total SOC pool in the topsoil (0-20 cm) of China was estimated at 60.02 Pg and 57.6 Pg.Thousands of years of intensive cultivation in China resulted in CP topsoil SOC loss of 4.34-4.98 Pg.

Behavior of wood basic density according to environmental variables

The relationships between climate conditions and wood density in tropical forests are still poorly understood.To quantify spatial dependence of wood density in the state of Minas Gerais(MG,Brazil),map spatial distribution of density,and correlate density with climate variables,we extracted data from the Forest Inventory of Minas Gerais for 1988 trees scaled throughout the territory and measured wood density of discs removed from the trees.Environmental variables were extracted from the database of the Ecological-Economic Zoning of Minas Gerais.For spatial analysis,tree densities were measured at 44 georeferenced sampling points.The data were subjected to exploratory analysis,variography,cross-validation,model selection,and ordinary kriging.The relationships between wood density and environmental variables were calculated using dispersion matrices,linear correlation,and regression.Wood density proved to be highly spatially dependent,reaching a correlation of 96%,and was highly continuous over a distance of 228 km.The distribution of wood density followed a continuous gradient of 514-659 kg m^(−3),enabling corre-lation with environment variables.Density was correlated with mean annual precipitation(−0.57),temperature(0.63),and evapotranspiration(0.83).Geostatistical methods proved useful in predicting wood density in native tropical forests with different climate conditions.Our results confirmed the sensitivity of wood density to climate change,which could affect future carbon stock in forests.

Impacts of climatic and marine environmental variations on the spatial distribution of Ommastrephes bartramii in the Northwest Pacific Ocean

Ommastrephes bartramii is an ecologically dependent species and has great commercial values among the AsiaPacific countries. This squid widely inhabits the North Pacific, one of the most dynamic marine environments in the world, subjecting to multi-scale climatic events such as the Pacific Decadal Oscillation（PDO）. Commercial fishery data from the Chinese squid-jigging fleets during 1995-2011 are used to evaluate the influences of climatic and oceanic environmental variations on the spatial distribution of O. bartramii. Significant interannual and seasonal variability are observed in the longitudinal and latitudinal gravity centers（LONG and LATG） of fishing ground of O. bartramii. The LATG mainly occurred in the waters with the suitable ranges of environmental variables estimated by the generalized additive model. The apparent north-south spatial shift in the annual LATG appeares to be associated with the PDO phenomenon and is closely related to the sea surface temperature（SST）and sea surface height（SSH） on the fishing ground, whereas the mixed layer depth（MLD） might contribute limited impacts to the distribution pattern of O. bartramii. The warm PDO regimes tend to yield cold SST and low SSH, resulting in a southward shift of LATG, while the cold PDO phases provid warm SST and elevated SSH,resulting in a northward shift of LATG. A regression model is developed to help understand and predict the fishing ground distributions of O. bartramii and improve the fishery management.