Seasonal Time Research Articles

Determining Spatial Responses of Fishers (Pekania Pennanti) to Mechanical Treatments of Forest Stands for Fuel Reduction

Historical forestry practices (e.g., fire suppression, heavy timber logging) have contributed to a discernable change in stand composition of western forests in the U.S., which now comprise a tinderbox mixture of increased surface and ladder fuels, dense stands, and fire-intolerant species. Forest managers are mitigating this concern by implementing silviculture practices (e.g., selective logging, thinning, prescribed burning) to reduce fuel loads and improve stand resiliency. Concern for habitat specialists, such as the fisher (Pekania pennanti), have arisen as they may be negatively influenced in the short-term by modifications to their environment that are needed to ensure long-term habitat persistence. To address this issue, we initiated an 8-year study in 2010 in Ashland, Oregon, to determine the behavioral response of fishers to fuel reduction treatments applied in forested stands. We measured the distance of each location from eight GPS-collared fishers to all treatments before and after they were treated within each home range, and performed three statistical tests for robustness, including a multi-response permutation procedure, chi-squared test of independence, and a Kolmogorov–Smirnov assessment. We found high variation among individuals to the tolerance of habitat manipulation. Using effect size to interpret the magnitude of fisher response to pre- and post-treatment effects, 1 fisher showed a moderate negative relationship to fuel reduction treatments, 5 exhibited a weak negative response, and 2 had a weak positive association with treatments. We used analysis of variance on the three fishers exhibiting the largest effect sizes to treatment disturbance, and used treatment, temporal, and habitat covariates to explore whether these factors influenced behavioral differences. Treatment season and vegetation class were important factors influencing response distance in the pre-treatment period. Post-treatment variables eliciting a negative treatment response were treatment season and treatment size, and results were slightly different when parsing out individual effects compared to a pooled sample set. Our findings suggested that seasonal timing and the location of management activities could influence fisher movement throughout their home range, but it was largely context-dependent based on the perceived risks or benefits to individuals.

A small area analysis of acute exposure to temperatures and mental health in North Carolina.

Increasing evidence suggests that temperatures adversely impact mental and behavioral disorders (MBD). This study explores the effects of temperatures on mental health outcomes using over 5.9million MBD-related emergency department (ED) visits across three geographical regions of North Carolina (i.e., Mountains, Piedmont, and Coast) from 2016 to 2019. A distributed lag non-linear model (DLNM) with a generalized linear model and quasi-Poisson distribution adjusted for humidity, long-term seasonal time trends, and day of the week examined the acute impact (i.e., 7-day) of temperature on daily MBD-related ED visits at zip code tabulation area (ZCTA) locations. Results were pooled at the region and state levels and reported in reference to the median temperature using a case-time series design for the analysis of small-area data. Stratified analyses were conducted for age, sex, and specific mental-health related ED visits (substance use, mood disorders, anxiety disorders). At the state level, we found significant positive associations between high temperatures (97.5th percentile) and an increase in relative risk (RR) for total MBDs (RR:1.04, 95% CI,1.03-1.05) and psychoactive substance use (RR:1.04, 95% CI, 1.02-1.06). Low air temperatures (2.5th percentile) only increased risk for the elderly (i.e., 65 and older) and predominantly white communities (RR: 1.03, CI: 1.03-1.05). During high temperatures (97.5th percentile), majority-white communities (RR:1.06, CI: 1.01-1.10) and low-income communities had the highest risk for MBDs (RR: 1.05, CI: 1.03-1.07). Our findings suggest there is a positive association between exposure to high temperatures and increased MBD-related ED visits, modified by patient age and place-based sociodemographic (ie., race and income) context.

A review in pollen grains for plants

Ripe pollen's primary morphological, cytological, and physiological characteristics are explained, contrasted, examined, and explored alone, in various combinations, and in relation to the female counterpart as well as the biotic and abiotic environmental factors. The goal is to attempt and figure out why pollen grains have the same reproductive function yet differ greatly in morphology and physiology during dispersal. One or more grain types per species, size and shape, number of cells, pollen dispersal unit types, sporoderm stratification, furrows, colpori, and other apertures, pollen presentation and array, water content percentage, mature pollen reserves, and osmotics are among the characteristics taken into consideration. Some pollen characteristics are related to each other, while others are tied to the female counterpart or competition between males and females, and still others are related to the many elements of the species' habitat, flowering season, and pollen presentation time.

Spatio-Temporal Agnostic Sampling for Imbalanced Multivariate Seasonal Time Series Data: A Study on Forest Fires

Natural disasters are mostly seasonal and caused by anthropological, climatic, and geological factors that impact human life, economy, ecology, and natural resources. This paper focuses on increasingly widespread forest fires, causing greater destruction in recent years. Data obtained from sensors for predicting forest fires and assessing fire severity, i.e., area burned, are multivariate, seasonal, and highly imbalanced with a ratio of 100,000+ non-fire events to 1 fire event. This paper presents Spatio-Temporal Agnostic Sampling (STAS) to overcome the challenge of highly imbalanced data. This paper first presents a mathematical understanding of fire and non-fire events and then a thorough complexity analysis of the proposed STAS framework and two existing methods, NearMiss and SMOTE. Further, to investigate the applicability of STAS, binary classification models (to determine the probability of forest fire) and regression models (to assess the severity of forest fire) were built on the data generated from STAS. A total of 432 experiments were conducted to validate the robustness of the STAS parameters. Additional experiments with a temporal data split were conducted to further validate the results. The results show that 180 of the 216 binary classification models had an F1score>0.9 and 150 of the 216 regression models had an R2score>0.75. These results indicate the applicability of STAS for fire prediction with highly imbalanced multivariate seasonal time series data.

AN OVERVIEW OF THUNDERSTORMS OVER BANGLADESH

An attempt has been made to find out the monthly, seasonal and annual variability, and trend of thunderstorms (TSs) over Bangladesh, specialty the pre-monsoon TSs which are locally known as Nor’westers or Kal-baishakhis. TSs data for the period of 1981-2020, collected from Bangladesh Meteorological Department (BMD), have been analyzed and found that during this period average annual TS days were 66 having maximum in May and minimum in December. Seasonal distribution of TSs shows that highest and lowest number of TSs occurred in the monsoon season and winter season respectively but the pre-monsoon TSs are severe in nature. From the seasonal time series of TSs, it is seen that all the three seasons have increasing trend and the winter season shows decreasing trend. Station-wise distribution of annual average TS days reveals that Sylhet and Srimangal experience the highest number of TS days (136.28 and 116.92 days respectively), while Teknaf experiences the lowest number of TS days (26.15 days) during the study period (1981-2020). Spatial distribution of average annual and seasonal TS days shows that, the North-eastern part of the country is the most TS prone area, followed by northern, mid-western and north-western parts of the country. South-eastern part of the country experienced comparatively lower number of annual average TSs days. Most of the Nor’westers directed from north-west and occurred between 1400- 1600 U.T.C. and least number of Nor’westers occurred between 0400-0600 U.T.C. As the number of observatories of Bangladesh Meteorological Department (BMD) is less considering its area, there is possibilities of unnoticed thunderstorms over Bangladesh. Bangladesh Journal of Physics, Vol. 29, Issue 1, pp. 1 – 11, June 2022

Seasonal plasticity in daily timing of flight activity in Anopheles stephensi is driven by temperature modulation of dawn entrainment.

The Asian malaria vector Anopheles stephensi is invading Africa, requiring it to adapt to novel climates and ecosystems. In part, this may be facilitated by An. stephensi's poorly understood seasonal behavioural plasticity in flight timing, leading to earlier biting activity in cold Asian winters and later biting times in the warm summer. Changes in behavioural timing could be directly imposed by seasonal variation in ambient light and temperature levels or result from altered entrainment of intrinsically expressed circadian rhythms by these factors. We demonstrate that An. stephensi entrained flight activity timing is phase-locked to dawn and is not affected by constant ambient temperature, which cannot explain earlier biting activity in colder winters with later dawn. Instead, we show that where night temperatures are the colder part of daily temperature cycle; the entrained phase-angle between dawn and flight activity is altered, hereby increasingly colder, winter-like nights progressively advance flight activity onset. We propose that seasonal timing plasticity optimizes behaviour to warmer daytime in winter, and colder nights in summer, providing protection against both heat-desiccation and cold immobility. The adaptive advantage of this plasticity could be relevant to the successful invasion and survival of An. stephensi in African climates, and changing climate worldwide.This article is part of the Theo Murphy meeting issue, 'Circadian rhythms in infection and immunity'.

Response of yield and associated traits of wheat genotypes to temperature fluctuations at Dera Ismail Khan

Wheat is the third largest staple food crop accounting for approximately 4% of cropland and its demand is continuously increasing to feed the growing world population. Average surface temperature and recurrent heat waves are continuously rising from last few decades that adversely influence yield potential of various crops including wheat. Temperature differences can be fashioned by cultivating at diverse planting times. Aim of current investigation was to appraise impact of planting times and thermal high temperature (growing degree days) on yield of 8 cultivars. Crop was exposed to four planting times in wheat crop season 2022-23. Experiment was carried out in randomized complete block (RCB) design with 3 replications. Results revealed that planting times depicted significant variances for all considered yield and associated traits. Similarly, cultivars too exhibited highly significant differences for plant height, physiological maturity, spike length, spikelets/spike, grains/spike, thousand grain weight and grain yield/plant however 50% days to heading and tillers per plant depicted non-significant differences among genotypes. Interaction of planting time to genotypes was also significant for 50% days to heading, days to physiological maturity, spike length, spikelets/spike, grains/spike and 1000 grain weight however it was non-significant for plant height, tillers/plant and grain yield per plant. Cultivar AZRC-Dera produced highest grain yield/plant on account of maximum spikelets, grains spike-1, tillers and thousand grain weight. Highest grain yield was recorded in planting times from 1st November to 20th November due to accumulation of more heat units whereas late cultivation reduced production on account of less accumulation of heat units. Therefore it is determined that wheat should be planted from 1st November to 20th November in Dera Ismail Khan to have good harvest

Evaluation and Adjustment of Historical Hydroclimate Data: Improving the Representation of Current Hydroclimatic Conditions in Key California Watersheds

The assumption of stationarity in historical hydroclimatic data, fundamental to traditional water resource planning models, is increasingly challenged by the impacts of climate change. This discrepancy can lead to inaccurate model outputs and misinformed management decisions. This study addresses this challenge by developing a novel monthly data adjustment approach, the Runoff Curve Year–Type–Monthly (RC-YTM) method. The application of this method is exemplified at five key California watersheds. The RC-YTM method accounts for the increasing variability and shifts in seasonal runoff timing observed in the historical data (1922–2021), aligning it with the contemporary climate conditions represented by the period from 1992 to 2021 at the study watersheds. This method adjusts both annual and monthly streamflow values using a combination of precipitation–runoff relationships, quantile mapping, and water year classification. The adjusted data, reflecting current climatic conditions more accurately than the raw historical data, serve as valuable inputs for operational water resource planning models like CalSim3, commonly used in California for water management. This approach, demonstrably effective in capturing the observed climate change impacts on streamflow at monthly timesteps, enhances the reliability of model simulations representing contemporary conditions, which can lead to better-informed decision-making in water management, infrastructure investment, drought and flood risk assessment, and adaptation strategies. While focused on specific California watersheds, this study’s findings and the adaptable RC-YTM method hold significant implications for water resource management in other regions facing similar hydroclimatic challenges in a changing climate.

Abrupt transformation of West Greenland lakes following compound climate extremes associated with atmospheric rivers

Arctic ecosystems are affected by accelerated warming as well as the intensification of the hydrologic cycle, yet understanding of the impacts of compound climate extremes (e.g., simultaneous extreme heat and rainfall) remains limited, despite their high potential to alter ecosystems. Here, we show that the aquatic ecosystems in historically arid west Greenland have undergone an ecological transformation after a series of atmospheric rivers that simultaneously produced record heat and rainfall hit the region in autumn 2022. We analyzed a unique, long-term lake dataset and found that compound climate extremes pushed Arctic lakes across a tipping point. As terrestrial-aquatic linkages were strengthened, lakes synchronously transformed from "blue" lakes with high transparency and low pelagic primary production to "brown" in less than a year, owing to a large influx of dissolved organic material and metals, with iron concentrations increasing by more than two orders of magnitude. The browning of lake waters reduced light penetration by 50% across lakes. The resulting light limitation altered plankton distributions and community structure, including a major reduction in prokaryotic diversity and an increase in algal groups capable of metabolizing organic carbon sources. As a result, lakes shifted from being summer carbon sinks to sources, with a >350% increase in carbon dioxide flux from lakes to the atmosphere. The remarkably rapid, coherent transformation of these Arctic ecosystems underscores the synergistic and unpredictable impacts of compound extreme events and the importance of their seasonal timing, especially in regions with negative moisture balance.

Impact of seasonal malaria chemoprevention timing on clinical malaria incidence dynamics in the Kedougou region, Senegal.

Seasonal malaria chemoprevention (SMC) with sulfadoxine-pyrimethamine and amodiaquine is recommended by the World Health Organization since 2012 for clinical malaria prevention in children in the Sahelian region of Africa. In Senegal, SMC implementation began in 2013 and is given to children under 10 years old. This study aimed to describe clinical malaria incidence in the general population during routine SMC implementation and to analyse how SMC timing impacted clinical malaria dynamics in eligible children. We conducted an ecological study in the Kedougou region of Senegal in 27 villages included in the Bandafassi Health and Demographic Surveillance System (HDSS). We calculated weekly Plasmodium falciparum malaria incidence by age group using malaria case data recorded by community health workers and health-posts, and population denominators obtained from Bandafassi Health and Demographic Surveillance System. We used negative binomial generalized additive multilevel models to analyse the incidence of clinical episodes in children under 10 years during the expected SMC prophylactic period and at the end of the transmission period. Malaria incidence was strongly seasonal with a high transmission period starting in June. Children under SMC presented an overall lower incidence compared to older children and young adults. Among children eligible for SMC, the incidence was lowest for approximately 3 weeks after treatment administration and increased subsequently, suggesting a gradual loss of protection. At the end of the high transmission period, a higher malaria incidence was recorded from the 3rd to 6th week after the week of administration of the fourth (final) SMC round. While protecting children under 10 years, SMC warrants adjustment to reduce exposure before the next round, to increase protection of 5-9 years, and to cover the high transmission period completely. The addition of a 5th round of SMC in 2023 was necessary to cover the end of the transmission season, but individual-level studies are required to ensure that drug efficacy and adequate dosing are maintained.

Hydro-Climatic Trends in Central Italy: A Case Study from the Aterno-Pescara River Watershed

Climate change is reshaping water systems and trends in hydro-climatic variables, such as temperature, precipitation, and river runoff, providing critical insights into the hydrological shifts influenced by climate change. However, the impact of climate variability on these variables varies by geographic location, making it necessary to study hydro-climatic variations in the Mediterranean’s changing climate to determine its impacts. This study analyzed the hydro-climatic trends in the Aterno-Pescara River watershed in central Italy from 1936 to 2013. We employed linear regression, Mann–Kendall, Sen’s slope, and Spearman correlation tests to estimate annual and seasonal trends. The results showed a significant warming trend on annual (0.037 °C/year) and seasonal time scales. Precipitation trends exhibited significant reductions annually, specifically during the autumn season, with a decrease of −0.68 mm/year; while showing a decline, other seasons were statistically insignificant. River runoff revealed drying trends annually and seasonally, decreasing by −0.29 m3 s−1/year over the study period. Furthermore, linear regression and Spearman correlation coefficients suggested a significant relationship between hydro-climatic variables with varying strengths (at 95% and 99% confidence levels) annually and seasonally. This decrease in precipitation and river runoff trends with the continuous rate points towards potential meteorological and hydrological droughts occurring in the future in this watershed. This study’s findings provide scientific grounds that could help develop sustainable strategies in the watershed.

Assessing coupling between soil temperature and potential air temperature using PALM-4U: implications for idealized scenarios

Abstract. Underground heat extremes, amplified by factors such as underground infrastructure or poorly adjusted geothermal systems, have long been discussed in the geosciences. However, there is little emphasis on the exchange between these subsurface heat extremes and the atmosphere. To address the issue, this study investigates the impact of varying soil temperatures on potential air temperatures in an idealized domain using the turbulence- and building-resolving large-eddy-simulation urban microclimate model PALM-4U (Parallelized Large-Eddy Simulation Model for Urban Applications). This involves two steps. First, we test if and how idealized domains can be simulated, and second, the coupling between surface and subsurface energy fluxes, or rather temperatures in air and soil, is in focus. We develop several scenarios, distinguishing between cyclic and Dirichlet/radiation boundary conditions along the x axis, between summer and winter, and between various land cover types. Our results demonstrate that cyclic boundary conditions induce modifications in potential air temperatures due to changes in soil temperature. The magnitude of the impact varies with respect to the tested land covers, which primarily affect absolute temperatures. The time of day and season have a larger influence on the magnitude of the modifications. A 5 K increase in subsurface temperatures at 2 m depth results in a maximum increase of 0.38 K in near-surface potential air temperatures during winter between 09:00 and 10:00 local time after 3 d of simulation. When soil temperatures are decreased, we find predominantly inverse patterns. The least influence is found during summer at 09:00, when elevated soil temperatures increase potential air temperatures by only 0.02 K over short and tall grass and by 0.18 K over bare soil. When using Dirichlet/radiation boundary conditions, the atmosphere cannot develop freely, and changing soil temperatures do not impact potential air temperatures. These results help enhance our understanding of the coupling between soil and atmospheric temperatures and also provide recommendations for the “simulatability” of idealized but reality-oriented scenarios in PALM-4U. This is one of the first studies to demonstrate that heat and cold sources in the soil can affect atmospheric parameters.

Decoding Wildlife Habitat Shifts in a Changing Environment Through Machine Learning

Climate change affects wildlife habitats. Data analytics and machine learning have been used in this study, which focused on different ecosystems in the state of Virginia. In this paper, we propose a holistic approach that integrates climate data from the Open-Meteo API and wildlife observations from the iNaturalist API during the years 2020-2023 using real-time data collection, machine learning models, and interactive visualization techniques. Our approach uses ensembling machine learning methods such as XGBoost, Random Forest, and Gradient Boosting classifiers with 85% accuracy using only climate variables as predictors of wildlife presence. Among them, very strong correlations between temperature patterns and observations of wildlife were found (r = 0.72, p < 0.001); temperature range and seasonal timing explained about 65% of the model fit. We developed an interactive web-based dashboard using Dash that visualizes temporal trends and spatial distributions. This study informs conservation planning and habitat management decisions in the face of climate change and demonstrates how the integration of multi-analytical approaches and real-time data collection provides detailed insights into complex ecological relationships.

Insect size responses to climate change vary across elevations according to seasonal timing.

Body size declines are a common response to warming via both plasticity and evolution, but variable size responses have been observed for terrestrial ectotherms. We investigate how temperature-dependent development and growth rates in ectothermic organisms induce variation in size responses. Leveraging long-term data for six montane grasshopper species spanning 1,768-3 901 m, we detect size shifts since ~1960 that depend on elevation and species' seasonal timing. Size shifts have been concentrated at low elevations, with the early emerging species (those that overwinter as juveniles) increasing in size, while later season species are becoming smaller. Interannual temperature variation accounts for the size shifts. The earliest season species may be able to take advantage of warmer conditions accelerating growth during early spring development, whereas warm temperatures may adversely impact later season species via mechanisms such as increased rates of energy use or thermal stress. Grasshoppers tend to capitalize on warm conditions by both getting bigger and reaching adulthood earlier. Our analysis further reinforces the need to move beyond expectations of universal responses to climate change to consider how environmental exposure and sensitivity vary across elevations and life histories.

Serenade of a whimbrel: understanding the function of display behaviour in a sub‐Arctic territorial wader

Animal sounds contain important information used in intra‐ and inter‐species communication. For species exhibiting elaborate and energetically expensive signals such as aerial displays accompanied by a call, the nature of the message being transmitted is honest and usually reflects individual fitness. Display events that combine flight and calls and are executed during the breeding season have traditionally been associated with two main functions: repelling rivals and/or attracting mates. In waders, the distinction between the two may rely on the timing of the breeding season at which displays occur, but also on the frequency at which displays occur in relation to local breeding density. Here, we investigated the function of display behaviour in the Icelandic whimbrel throughout the breeding season and along a breeding density gradient. We used a site‐based approach in the southern lowland plain in Iceland where whimbrel breeding density and display frequency were recorded for two years. Although whimbrels showed display behaviour throughout the entire season, display frequency was higher at low densities during pre‐incubation but similar at different densities during incubation and post‐incubation. Our results indicate that this behaviour is context‐dependent and multi‐functional, supporting the importance of mate attraction at low conspecific density during pre‐incubation, while other functions such as resource defence may be important throughout the breeding season.

Isotopes unveil overestimation of nutrient-driven oxygen deficit in the tidal rivers of Pearl River Delta during the wet season.

A low dissolved oxygen (DO) concentration in summer has been observed in river-estuary systems worldwide. Many studies have caused our stereotype that biochemical oxygen depletion was higher in summer than in winter; however, there was no direct evidence particularly in the tidal river with complex hydrological and biochemical processes. This study employed natural-abundance and labeled isotopes to quantify seasonal apportionment of biochemical oxygen depletion. In this study, apparent oxygen utilization (AOU) and carbon (C) and nitrogen (N) turnover potentials (nitrification rates and δ13C signals) were higher in the wet season than in the dry season. However, calculation results of the nitrification flux demonstrated that actual N turnover was constrained by shorter river residence time in the wet season. Similarly, the δ13C end-member mixing and Rayleigh fractionation models revealed that the conservative C behavior was more pronounced than degradation in situ in the river channel. Overall, C- and N-driven oxygen depletion accounted only for ~8% of AOU in the wet season. This substantiated that the hydrological control regulated C and N behaviors to "the conservative transport" to mitigate O2 depletion in the wet season. In contrast, a good correspondence between C and N turnover and low oxygen was recorded in the dry season. Therefore, the "nutrient- and non-nutrient-constrained DO cold spots" during the dry and wet seasons provided new insights into oxygen deficits in tidal rivers. Our study provided compelling evidence that seasonal apportionment of C- and N-driven oxygen depletion in situ has changed in tidal rivers. Biochemical oxygen depletion was more evident in the dry season than in the wet season; thus, it had been previously overestimated in the wet season, which will provide implications for using different water management strategies in different seasons.

E_GSMaP precipitation dataset reforecasted by RF-WMRA: Description and validation.

Accurate precipitation data is an important state variable in various application fields such as geohazard early warning, flood, and drought hazard monitoring and evaluation. This study proposed a precipitation reforecast inversion model based on Random Forest and Wavelet Multi-Resolution Analysis (RF-WMRA) method. In the basin along the Sichuan-Tibet Railway, we constructed the reforecast inversion model by using the fifth generation of atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5) and Global Satellite Mapping of Precipitation (GSMaP) precipitation data and considering several geo-environmental variables such as elevation, humidity, temperature, and wind speed. In turn, we could obtain a set of E_GSMaP precipitation data with high spatial resolution (0.1°) from 1961 to 2020 in the study area. In addition, we also compared and analyzed the accuracy of the E_GSMaP data with both the ERA5 and GSMaP data from the perspective of extreme precipitation event monitoring. The results showed that: 1) The Correlation Coefficient (CC) of the RF reforecast model was 0.89, Root Mean Squared Error (RMSE) was 1.65mm, Relative Bias (BIAS) was 0.01%, Mean Absolute Error (MAE) was 0.42mm, and Kling-Gupta efficiency (KGE) was 0.80. CC and KGE increased to various degrees before and after WMRA model correction, RMSE, BIAS, and MAE decreased to various degrees. It indicated that this study's RF-WMRA reforecast inversion model performed better. 2) If extreme precipitation events in the study area were analyzed, we recommend the E_GSMaP data given the performance of three datasets, E_GSMaP, ERA5, and GSMaP, on seasonal and monthly time scales as well as in total extreme precipitation. The results of this study can provide basic data for regional geological disasters and hydrometeorological research and help to respond to the risk of geological disasters and climate change.

A seasonal matrix population model for ixodid ticks with complex life histories and limited host availability.

Many vector-borne diseases are sensitive to changes in land use and climate; hence, it is important to understand the factors that govern the vector populations. Ixodid ticks, which serve as vectors for multiple diseases, have a slow life cycle compared with many of their hosts. The observable questing population represents only a fraction of the total tick population and may include overlapping cohorts in each stage. The duration of each life stage (larvae, nymph, and adult) is variable and depends on factors such as the seasonal timing of questing, development, and host availability. Mathematical models are therefore essential to mediate how complex life cycle transitions and host interactions underpin the seasonal dynamics of the questing tick population. In this study, we develop a seasonal matrix population model for ixodid ticks feeding on a small and large host. The model has 17 stages representing the main life history stages (eggs, larvae, nymphs, and adults) combined with status of feeding, seasonal timing of feeding, and overwintering. The probability of finding a host depends on tick instar and host type, and density regulation is incorporated through limited host capacity. Using a life history representing Ixodes ricinus in Northern Europe as a baseline, we extract seasonal numbers of different parts of the tick population and calculate life history outcomes such as generation time and mean and variance of lifespan and of lifetime reproductive output. These results are compared with an alternative scenario of a southern life history. Secondly, we investigate (1) effects of seasonality in the small host availability on the seasonal numbers of tick stages and (2) effects of varying host availability and utilization of small versus large hosts by larvae and nymphs, on the seasonal numbers of questing ticks. Our results suggest that the small host availability is an important regulating factor through the feeding of larvae. Our model incorporates complex mechanisms underlying the seasonal composition of the tick population. It can be applied to different ixodid tick species and provides a framework for future investigations into intra- and interspecific variation in life history and population dynamics.

ИСПОЛЬЗОВАНИЕ НАБЛЮДЕНИЙ АРГО ДЛЯ ИССЛЕДОВАНИЯ МЕЖГОДОВОЙ ИЗМЕНЧИВОСТИ ТЕПЛООБМЕНА ПОЛЯРНЫХ МОРЕЙ С АТМОСФЕРОЙ, АТЛАНТИЧЕСКИМ И СЕВЕРНЫМ ЛЕДОВИТЫМ ОКЕАНАМИ И ИНТЕНСИВНОСТИ ЛЕДООБРАЗОВАНИЯ

The annual mean temperature, salinity, density, and velocity fields for the region of the Nordic Seas were calculated and studied using the Argo-based model for the period of 2005–2014. The heat transports through the Denmark and Fram Straits, and through the sections separating the Nordic Seas from the Atlantic Ocean and Barents Sea were studied on the climatological, annual, and seasonal time scales. The calculated transports are in good agreement with the previous estimations. The methodology of the mean ice formation rate estimation is discussed.

Regulation of hatching year by seasonal timing of oviposition in the stick insect, Phraortes elongatus (Phasmatodea: Phasmatidae)

Regulation of hatching year by seasonal timing of oviposition in the stick insect, Phraortes elongatus (Phasmatodea: Phasmatidae)