Maximum Summer Temperatures Research Articles

Forestry influences on salmonid habitat in the North Thompson River watershed, British Columbia

Freshwater ecosystems that support juvenile salmonids can be degraded by human pressures such as forestry. Forestry activities can alter water temperatures and the delivery and storage of water, nutrients, wood, and sediment in streams, resulting in changes to the habitat, growth, and survival of juvenile salmon. Previous research on forestry impacts on habitat has focused on small, intensively monitored coastal systems. Here, we examined forestry activities, watershed characteristics, physical habitat, and stream temperature for 28 mid-sized tributaries of the North Thompson River to examine relationships between forestry and juvenile coho stream habitat in interior watersheds. Forest harvest had a positive correlation to maximum summer stream temperature. Streams with 35% of the riparian area harvested since 1970 had maximum summer temperatures 3.7 °C higher on average than those with 5% harvested. Stream gradient explained most of the variation in physical habitat and had negative correlations to pool cover, pool depth, and fine sediment cover. Taken together, these results indicate that watershed characteristics drive physical habitat, but forest harvest can be a primary driver of water temperatures.

Comparison of environment quality measurements between 3 types of calf housing in the United Kingdom

Preweaning calves are kept in a range of housing types that offer variable protection against the weather and provide differing internal environments. This cross-sectional observational study assessed the effect of housing type (shed, polytunnel, or hutches) on internal environmental parameters, using 2 blocks of 8-wk measurements from 10 commercial dairy farms in the south of England, covering both summer and winter periods. Continuous measurements for internal and external temperature and humidity were recorded by data logger placed within the calf housing and used to calculate the temperature-humidity index (THI). Weekly point readings were also taken for temperature, humidity, light, air speed, ammonia level, and airborne particulate matter. Airborne bacterial levels were determined at wk 2, 5, and 8 by incubating air samples at 35°C for 24 h in aerobic conditions. Data were analyzed using linear mixed models. Housing type influenced THI significantly in both seasons. In summer, calves were exposed to heat stress conditions (THI ≥72) for 39, 31, and 14 of 46 d in polytunnel housing, hutches, and sheds, respectively. The maximum summer temperature (37.0°C) was recorded in both hutch and polytunnel housing, with sheds remaining consistently cooler (maximum 31.0°C). In winter, the lowest minimum internal temperature recorded was in hutches at -4.5°C, with both the sheds and polytunnel, but not hutches, providing a significant increase in temperature compared with the external environment. Hutches remained ≤ 10°C for 86% of the winter study period. Light levels were reduced in all housing types compared with the external environment. The particulate matter in air that is capable of reaching the lungs (particulate matter <10 μm) was highest in sheds, intermediate in hutches, and lowest in polytunnel housing (0.97 ± 3.75, 0.37 ± 0.44, and 0.20 ± 0.24 mg/m3, respectively). This was mirrored by airborne bacterial numbers, which were also highest in sheds (8,017 ± 2,141 cfu/m3), intermediate in hutches (6,870 ± 2,084 cfu/m3), and lowest in the polytunnel (3,357 ± 2,572 cfu/m3). Round, white, catalase-positive, and oxidase-negative colonies were most prevalent, likely indicating Staphylococcus species. This study demonstrated that UK calves are routinely exposed to either heat or cold stress, especially when housed in hutches or polytunnels. Sheds had the highest levels of particulate matter and airborne bacteria, both known contributory factors for respiratory disease. These findings demonstrate that all calf housing systems result in environmental compromises that could have long-term impacts on calf health and growth; therefore, further studies should identify husbandry and housing modifications to mitigate these factors.

Predicting Iran’s future agro-climate variability and coherence using zonation‑based PCA

The effects of climate changes on agroecosystems can cause relevant issues. Using principal component analysis (PCA) we determined the 67 agricultural climate indicators (ACI) at 44 of Iran’s synoptic stations under current (1990-2019) and future (2025, 2050, 2075, and 2100) conditions. Based on UNESCO aridity index, the agroecological zonation (AEZ) was used to classify Iran’s regions (very dry, dry, semidry and humid climates). Using the PCA method, the first 5 principal components were determined by including data sets for temperature (winter, spring, summer and autumn maximum and winter minimum temperature), precipitation (winter and summer precipitation), reference evapotranspiration (ETref), and the degree of growth days in spring and winter, which explained about 96 percent of the total variance. For each climate empirical equation for ETref was selected. In order to accurate evaluation of ETref were used The Penman-Monteith based on FAO56 (PM-FAO56) for the very dry climate, the Hargreaves equation for the semidry climate, and the Penman 1 and 2 equations for the dry and humid climates, respectively. According to the results, the first component alone, with an eigenvalue of 41.15, explained more than 74 percent of the total variance. Based on the results of zoning by the PCA outcomes, the stations for 1990-2019 were classified into 7 zones. While 2025, 2050, 2075, and 2100 were classified in 6, 7, 6, and 5 zones, respectively. Under the future climatic conditions of the country, in terms of climatic indicators, the similarity between the stations will increase and the climatic diversity of the country will decline compared to current conditions. The results demonstrated that the PCA method would be valuable for monitoring AEZ in semidry climates at reasonably long periods.

Holocene vegetational and climatic history of the Xuguo Co catchment in the central Tibetan Plateau

A 101-cm core was taken from a large lake in the central Tibetan Plateau. Its pollen and loss-on-ignition analyses provide a Holocene vegetational, climatic, and environmental history of the lake catchment. Pollen analysis shows that: dense steppe dominated regional vegetation in the early Holocene (9,200–8,000 cal. yr BP); regional vegetation coverage gradually decreased in the middle Holocene (8,000–4,100 cal. yr BP); and marsh meadow grew on the lake edge and sparse steppe occupied the lake catchment after 4,100 cal. yr BP. Our result also reveals that: 9,200–8,000 cal. yr BP witnessed summer temperature, monsoonal rainfall, and lake-level maxima, as well as few winter and spring aeolian activities and frequent wildfires; 8,000–4,100 cal. yr BP saw a nonlinear decline in temperature, rainfall, lake level, and wildfires; and modern climatic and environmental conditions were established after 4,100 cal. yr BP. Three major monsoon-weakening events at ca. 6,700, 5,800, and 4,100 cal. yr BP were detected by pollen signals and proxies of the climate and environment.

Streamflow, Precipitation, and Temperature at Select Stationsin the Smokey Hills Watershed, Kansas

It is important for Kansans to understand how streamflow relates to precipitation and temperature in Kansas, as streamflow variability has implications for water resources, ecosystem health, and many other activities. Streamflow across eight stream gauges in the Smokey Hills Watershed is positively correlated with precipitation and negatively correlated with maximum summer temperature, most prominently in late spring and throughout summer. With climate variability and longer-term changes, it will continue to be paramount to document these relationships.

The relationship between climate variability and wheat yield in Tashkent province

This study focuses on the relationships between climate variability and wheat yield in Tashkent province. It contains a time series study of precipitation, temperature, and wheat yield, as well as the assessment of the link between yield and climatic factors and an examination of the influence of climate change on crop production using a regression model. Time series results of temperature present a positive trend in mean temperature in Bustanlik and Urtachirchik. Annual minimum temperature, the minimum temperature in spring, minimum temperature between September and May have changed significantly in Bustanlik over the years (p&lt;0.05). Total precipitation shows a favourable trend in Bustanlik but a negative trend in Urtachirchik. There is no significant change detected in the time series. The magnitude of the change in climate variables shows no clear tendency. Wheat yield has changed significantly and increased up to 1.34 c/ha every year between 1998-2014. The highest association is determined between wheat yield and minimum temperature in the growing season (0.77) while the highest correlation was identified with the summer maximum temperature (-0.41) in Urtachirchik. The linear multiple regression model forecasted the wheat yield with a mean error of 0.08 c/ha in Urtachirchik and 0.06 c/ha in Bustanlik district.

Spatial prioritisation of survey and management efforts for a threatened pygopodid in south-western New South Wales

Determining species’ distributions is challenging for cryptic species that are difficult to detect using standard techniques. The mallee worm-lizard (Aprasia inaurita Kluge, 1974) is a cryptic reptile in the family Pygopodidae, listed as Endangered in New South Wales. We modelled the species’ potential distribution (Maxent) to improve understanding of the species’ distribution and surveyed potential habitat in the Scotia Mallee region (an area with suitable habitat) from 2018 to 2022, with pitfall traps and artificial refuges (terracotta roof tiles). We completed 11 587 pitfall trap-nights and 3200 tile checks over eight monitoring sessions. Over this period, we detected six vertebrate species (all lizards) using roof tiles and 40 species with pitfall traps, but no mallee worm-lizards. Evaluation of existing records of the mallee worm-lizard from NSW suggested that the state constitutes the north-eastern edge of its continental range, with the species apparently present in low numbers across a wide swathe of south-western NSW. Most records were located within or near to spinifex or porcupine grass (Triodia spp.) communities, on private land. Species distribution modelling provided outputs that are useful for spatial prioritisation of conservation efforts for the species, with region-wide maps showing that much of the Scotia Mallee study area contains potentially suitable habitat for the mallee worm-lizard. However, habitat suitability scores for individual cells in this area were low, in some instances, because of high maximum summer temperatures and soil available water capacity. We anticipate that increasing temperatures associated with climate change may further reduce the suitability of habitat in this area in the future.

Homogenization of daily temperature and humidity series in the UK

AbstractBuilding on previous experience with continental and global data sets, we use a quantile‐matching approach to homogenize temperature and humidity series measured by a network of 220 stations in the United Kingdom (UK). The data set spans 160 years at daily resolution, although data coverage varies greatly in time, space, and across variables. We use the homogenized data to analyse trends of the mean values as well as the lowest and highest quantiles of the distribution over the last 100 and 50 years. For the latter period, we find large regional differences, particularly between the southeastern and the northern part of the UK. The southeast has seen a faster warming, particularly for maximum temperatures in spring and summer, and a reduction of relative humidity; the northern mainland has become more humid and only slightly warmer. These differences become more evident for the highest quantiles and reflect a well‐known pattern of climate change affecting the extra‐tropics. Among the studied variables, the increases of wet bulb temperature and specific humidity are the most spatially homogeneous and are statistically significant for most stations in all seasons except winter.

Changing Spring Phenology of Northeast China Forests during Rapid Warming and Short-Term Slowdown Periods

The vast forests of Northeast China are under great pressure from climate change. Understanding the effects of changing climate conditions on spring phenology is of great significance to assessing the stability of regional terrestrial ecosystems. Using Normalized Difference Vegetation Index data from 1982 to 2013, this paper investigated the changes in the start date of the vegetation growing season (SOS) of two main forest types in Northeast China, analyzing the changes in temporal and spatial patterns of forest spring phenology before and during the recent short-term warming slowdown, and exploring the effects of day and night temperatures and precipitation on the start of the growing season. The results showed that, during the rapid warming period (1982–1998), the SOS of deciduous needleleaf forests (DNF) was significantly advanced (−0.428 days/a, p &lt; 0.05), while the rate of advance of SOS of deciduous broadleaf forests (DBF) was nonsignificant (−0.313 days/a, p &gt; 0.10). However, during the short-term slowdown (1998–2013), the SOS of DBF was strongly delayed (0.491 days/a, p &lt; 0.10), while the change in SOS of DNF was not significant (0.169 days/a, p &gt; 0.10). The SOS was sensitive to spring maximum temperature for both forest types during the analysis period. Increases in winter precipitation influenced the SOS during the rapid warming period for DNF; this combined with the increase in the spring maximum temperature contributed to the advance in SOS. The decrease in the spring maximum temperature during the short-term slowdown, combined with a decrease in the previous summer maximum temperature, contributed to the rapid delay of SOS for DBF. DBF SOS was also more influenced by lagged effects of prior conditions, such as previous autumn to spring precipitation during the rapid warming period and previous summer maximum temperature during the short-term slowdown. In general, SOS was mainly determined by changes in daytime thermal conditions; DNF is more sensitive to temperature increases and DBF is more sensitive to decreases. Different regional climate conditions lead to differences in the distribution of DNF and DBF, as well as in the response of SOS to climate change during the rapid warming and short-term slowdown periods.

Northern piroplasmosis of domestic reindeer in Southern Yakutia

In South Yakutia, in the Aldansky district, at the end of July, more than 50 deer fell to the reindeer herd No. 7 of OAO KMNS "Khatystyr". Earlier in Central Yakutia, in 2008, an outbreak of pyroplasmosis was recorded for the first time in domestic reindeer of the farm of JSC NAOK "Taba" at the Tomtor site of the Gorny district and the YaGSHA Tabsylyn educational and production farm at the Kangkeme site of the Yakutsk city district. There was a mass death of the main deer population in the "Kangkeme" section up to 90% and "Tomtor" up to 30%. During laboratory studies, a parasitic disease, pyroplasmosis, was registered. Pyroplasmosis is one of the most severe and acute parasitic diseases of animals. Especially in the abnormal summer heat, blood parasites multiply very quickly, piroplasmids destroy red blood cells in the body of deer, the process of oxidation and intoxication occurs, bilirubin is formed from the released hemoglobin by complex transformations, 2-3 days after infection, in the absence of medical care, a lethal outcome is observed. Carriers of parasites can be ticks (Ixodidea, Ixodes ricinus) and other blood-sucking. Blood parasitic diseases (hemosporidiosis) of animals in Yakutia have not been previously recorded, a case of infection of domestic reindeer in 2008 in Central Yakutia is known. Isolated cases of tick attacks on people have been recorded in the Aldan, Neryungrinsky, Olekminsky and Suntarsky districts. A case of tick attack, infection and disease with tick-borne borreliosis of one person was registered. In the warm season, a distinctive feature of the temperature regime in most of the territory of Yakutia is a rapid increase in average daily temperatures in spring, and the maximum summer temperature can reach +38 ° C. At this time, there is a massive flight of blood-sucking insects and an attack of ticks on animals and people on the territory of Yakutia.

Shifting climate conditions affect recruitment in Midwestern stream trout, but depend on seasonal and spatial context

AbstractClimate change is a complex threat to freshwater ecosystems. Effects on aquatic species will likely differ among populations depending on seasonal and spatial context, which makes a detailed understanding of population responses to shifting climate conditions key to guiding strategic decision‐making. However, few empirical studies have tested for such context dependency on distinct populations across seasons or at large spatial scales. We used 26 years of standardized survey data on recreationally and economically important brook trout (Salvelinus fontinalis) and brown trout (Salmo trutta) in Wisconsin, USA to assess short‐ and long‐term variability in population trends, and quantify the influence of seasonal and spatial climate variability (air temperatures and precipitation) on annual recruitment strength (as indexed by young‐of‐year [YOY] relative abundance in summer). Some short‐term population fluctuations were spatially consistent across the state, indicative of broadscale environmental forcing. Over the longer term, average YOY and age 1 and older brook trout relative abundance has declined since 2006, especially for YOY, while brown trout abundance has substantially increased. The effects of climate conditions on recruitment varied by species, season, and latitude. Increasing maximum summer temperatures were associated with lower brook trout recruitment, but higher brown trout recruitment. The effect for both species was stronger at lower latitudes. Spring temperatures were positively related to brown trout recruitment at lower latitudes; in mid‐latitude and northern streams, they were related to increasing recruitment up to about 1 standard deviation, above which recruitment declined. High and low winter and spring precipitation were associated with precipitous declines in recruitment for both species. By contrast, summer precipitation was positively related to recruitment in northern streams for brook trout and southern streams for brown trout. Our results demonstrate that shifts in climate can affect recruitment in similar species differently depending on temporal (seasonal) and spatial (warm, southern regions compared with cool, northern regions) context. Given trout population trends in Wisconsin and climate projections for the Midwestern United States, location‐ and species‐specific management actions are needed that account for this context dependency. Management should aim to maximize the resiliency of populations to extreme climate conditions by buffering negative influences on recruitment.

Effects of long-term average temperature on cardiovascular disease hospitalizations in an American elderly population

BackgroundShort-term exposure to high or low temperatures is associated with increased mortality and morbidity. Less is known about effects of long-term exposure to high or low temperatures. Prolonged exposure to high or low temperatures might contribute to pathophysiological mechanisms, thereby influencing the development of diseases. Our aim was to evaluate associations of long-term temperature exposure with cardiovascular disease (CVD) hospitalizations. MethodsWe constructed an open cohort consisting of all fee-for-service Medicare beneficiaries, aged ≥65, living in the contiguous US from 2000 through 2016 (∼61.6 million individuals). We used data from the 4 km Gridded Surface Meteorological dataset to assess the summer (June–August) and winter (December–February) average daily maximum temperature for each year for each zip code. Cox-equivalent Poisson models were used to estimate associations with first CVD hospitalization, after adjustment for potential confounders. We performed stratified analyses to assess potential effect modification by sex, age, race, Medicaid eligibility and relative humidity. ResultsHigher summer average and lower winter average temperatures were associated with an increased risk of CVD hospitalization. We found a HR of 1.068 (95% CI: 1.063, 1.074) per IQR increase (5.2 °C) for summer average temperature and a HR of 1.022 (95% CI: 1.017, 1.028) per IQR decrease (11.7 °C) for winter average temperature. Positive associations of higher summer average temperatures were strongest for individuals aged <75 years, Medicaid eligible, and White individuals. Positive associations of lower winter average temperatures were strongest for individuals aged <75 years and Black individuals, and individuals living in low relative humidity areas. ConclusionsLiving in areas with high summer average temperatures or low winter average temperatures could increase the risk of CVD hospitalizations. The magnitude of the associations of summer and winter average temperatures differs by demographics and relative humidity levels.

Projection of Thermal Bioclimate of Egypt for the Paris Agreement Goals

This paper presents the likely changes in the thermal bioclimate of Egypt under the Paris Agreement, which aimed to restrict global warming to increase by 1.5–2.0 °C. A mean multi-model ensemble (MME) of eight global climate models were employed to evaluate the two shared socioeconomic paths (SSP) scenarios SSP1-1.9 and SSP1-2.6, which indicated scenarios for 1.5 and 2.0 °C rates of warming, respectively. The spatial distribution of the observed bioclimate indicated higher values in the south and southeast regions. The findings showed that there was an increase in Egypt’s mean temperature by rates of 1.3 and 1.5 °C for SSP1-1.9 and SSP1-2.6, respectively, with a higher increase in the southeast. The SSP1-2.6 scenario showed a gradual temperature rise with time, while SSP1-1.9 projected a decrease in the far future. The daily temperature variation decreased in the same region, but this effect was amplified in the north by 0.2 °C. The seasonality decreased by −0.8 to −2.3% without any shift in isothermality. The maximum summer temperature increased more (1.3–2.2 °C) than the minimum winter temperature (0.9–1.5 °C), causing an increase in inter-seasonal variability. Most bioclimatic indicators more rapidly rose in the north and northeast regions of Egypt, according to the geographical distribution of their projections.

Seasonal energetic physiology in the ark shell Anadara kagoshimensis in response to rising temperature

Knowledge of physiological responses of important shellfish species to rising temperatures is crucial in assessing the impacts of climate change on marine aquaculture production. The physiological components of energy balance that support growth performance were measured seasonally at different exposure temperatures in the ark clams (Anadara kagoshimensis) cultured in the shallow muddy bottom sediment in Yeoja Bay, Korea. We tested the effects of winter minimum (3–8°C) and summer maximum (23–28°C) temperature elevations on individual physiological processes (ingestion, respiration, egestion, and excretion) and the combined energetic physiology (scope for growth [SFG] and net growth efficiency [K2] measures). The seasonal cycle of dry flesh tissue weight (DW) was also investigated from January 2016 to November 2018, to compare its variation at contrasting cold vs. warm regimes. The rates of physiological components were related to DW, generating significant allometric equations. The weight exponents of the equations for ingestion rate and respiration rate were low at the winter minimum compared with the remaining season temperatures, indicating a higher thermal sensitivity in larger individuals. The physiological rates that were re-calculated for individual components based on estimates of the slope and intercept of the equations increased with increasing temperature, revealing an incapability of thermal acclimation and a temperature effect at seasonally different endogenous conditions. The thermal sensitivity (Q10) of the ingestion rate and respiration rate was reversed between the winter minimum and the summer maximum temperature elevations, yielding negative SFG and K2 values at 3 and 28°C. Furthermore, the interannual difference in the seasonal cycle of clam DW displayed variations in the period of increment prior to spawning and the post-spawning loss/recovery in association with its energy balance status in the winter and summer temperature conditions. Overall, these results indicate that warming is projected to affect physiological performance and the seasonal DW cycle of clams in different manners between winter and summer: physiological benefits and advanced weight gain vs. heat stress and progressive weight loss, respectively. The mechanistic adjustment of the clam energy balance across thermal conditions seems to explain the recent advancement in its seasonal biological cycle, as well as the failure in spat collection and the mass summer mortality observed at this culturing site.

A vegetation carbon isoscape for Australia built by combining continental-scale field surveys with remote sensing

ContextMaps of C3 and C4 plant abundance and stable carbon isotope values (δ13C) across terrestrial landscapes are valuable tools in ecology to investigate species distribution and carbon exchange. Australia has a predominance of C4-plants, thus monitoring change in C3:C4 cover and δ13C is essential to national management priorities.ObjectivesWe applied a novel combination of field surveys and remote sensing data to create maps of C3 and C4 abundance in Australia, and a vegetation δ13C isoscape for the continent.MethodsWe used vegetation and land-use rasters to categorize grid-cells (1 ha) into woody (C3), native herbaceous, and herbaceous cropland (C3 and C4) cover. Field surveys and environmental factors were regressed to predict native C4 herbaceous cover. These layers were combined and a δ13C mixing model was used to calculate site-averaged δ13C values.ResultsSeasonal rainfall, maximum summer temperature, and soil pH were the best predictors of C4 herbaceous cover. Comparisons between predicted and observed values at field sites indicated our approach reliably predicted generalised C3:C4 abundance. Southern Australia, which has cooler temperatures and winter rainfall, was dominated by C3 vegetation and low δ13C values. C4-dominated areas included northern savannahs and grasslands.ConclusionsOur isoscape approach is distinct because it incorporates remote sensing products that calculate cover beneath the canopy, the influence of local factors, and extensive validation, all of which are critical to accurate predictions. Our models can be used to predict C3:C4 abundance under climate change, which is expected to substantially alter current C3:C4 abundance patterns.

Autoregressive models for time series of random sums of positive variables: Application to tree growth as a function of climate and insect outbreak

We present a broad class of semi-parametric models for time series of random sums of positive variables. Our methodology allows the number of terms inside the sum to be time-varying and is therefore well suited to many examples encountered in the natural sciences. We study the stability properties of the models and provide a valid statistical inference procedure to estimate the model parameters. It is shown that the proposed quasi-maximum likelihood estimator is consistent and asymptotically Gaussian distributed. This work is complemented by simulation results and applied to time series representing growth rates of white spruce (Picea glauca) trees from a few dozen sites in Québec (Canada). This time series spans 41 years, including one major spruce budworm (Choristoneura fumiferana) outbreak between 1968 and 1991. We found significant growth reductions related to budworm-induced defoliation up to two years post-outbreak. Our results also revealed the positive effects of maximum summer temperature, precipitation, and the climate moisture index on white spruce growth. We also identified the negative effects of the climate moisture index in the spring and the maximum temperature of the previous summer. However, the model’s performance on this data set was not improved when the interactions between climate and defoliation on growth were considered. This study represents a major advance in our understanding of budworm–climate–tree interactions and provides a useful tool to project the combined effects of climate and insect defoliation on tree growth in a context of greater frequency and severity of outbreaks coupled with the anticipated increases in temperature.

A new approach to evaluate the risk of bark beetle outbreaks using multi-step machine learning methods

Bark beetles (Coleoptera: Curculionidae) alter forest ecosystem functioning through tree mortality, thus causing billions of dollars in economic and ecological damages worldwide. Dendroctonus frontalis Zimmermann is considered one of the most significant insect pest species of pine (Pinus spp.) trees in the United States (U.S.), Central America, and Mexico. To manage this threat, research has sought to predict and forecast outbreaks and identify high risk areas to focus on preventative management and reduce economic losses. Prior work has focused on using environmental predictors, but limitations have arisen regarding data availability and structure. Our research objective was to improve on current D. frontalis outbreak prediction models using contemporary modeling techniques. Beetle outbreak data were obtained from the United States Department of Agriculture Forest Service (USDA-FS) and were paired with three spatial–temporal beetle outbreak dynamics from the prior year, fifteen climate variables (DAYMET and WORLDCLIM) (temperature, radiation, wind, and water resources), two terrain attributes (NASA) (elevation and compound topographic index), and four vegetation indices [Moderate Resolution Imaging Spectroradiometer (MODIS)] [maximum and mean normalized difference vegetation index (NDVI)] as predictive features. Extreme gradient boosting was used to create two separate models that predicted the probability and magnitude of beetle outbreaks, which were used to create interpolated prediction maps for the southeastern U.S. The interpolated maps were combined to estimate outbreak risk (i.e., risk = probability of outbreak × magnitude of outbreak). Overall model accuracy was 87.7% when tested on an independent dataset. Distance to prior year outbreak and mean NDVI were the most important features when predicting the probability of outbreak, while summer maximum temperature, distance to prior year outbreak, and winter minimum temperature were the highest weighted features when predicting the magnitude. Results indicated that most of the southeastern U.S. was at low risk (<0.0001% damage per hectare) for the years 2008–2020. Risk was highest for 2012, 2016, and 2017. A few areas in Alabama, Georgia, northern Florida, and South Carolina contained stands at higher risk for damage (>0.01% per hectare) and some locations were at risk for >90% damage per hectare. Extreme gradient boosting paired with outbreak probability and magnitude performed well and is proposed as a solution for future bark beetle prediction and forecasting for more timely management strategies. The inclusion of climatic variables in outbreak models allows for forecasting the effects of future climate change on pine pest populations globally.

Topographic and vegetation drivers of thermal heterogeneity along the boreal-grassland transition zone in western Canada: Implications for climate change refugia.

Climate change refugia are areas that are relatively buffered from contemporary climate change and may be important safe havens for wildlife and plants under anthropogenic climate change. Topographic variation is an important driver of thermal heterogeneity, but it is limited in relatively flat landscapes, such as the boreal plain and prairie regions of western Canada. Topographic variation within this region is mostly restricted to river valleys and hill systems, and their effects on local climates are not well documented. We sought to quantify thermal heterogeneity as a function of topography and vegetation cover within major valleys and hill systems across the boreal–grassland transition zone.Using iButton data loggers, we monitored local temperature at four hills and 12 river valley systems that comprised a wide range of habitats and ecosystems in Alberta, Canada (N = 240), between 2014 and 2020. We then modeled monthly temperature by season as a function of topography and different vegetation cover types using general linear mixed effect models.Summer maximum temperatures (T max) varied nearly 6°C across the elevation gradient sampled. Local summer mean (T mean) and maximum (T max) temperatures on steep, north‐facing slopes (i.e., low levels of potential solar radiation) were up to 0.70°C and 2.90°C cooler than highly exposed areas, respectively. T max in incised valleys was between 0.26 and 0.28°C cooler than other landforms, whereas areas with greater terrain roughness experienced maximum temperatures that were up to 1.62°C cooler. We also found that forest cover buffered temperatures locally, with coniferous and mixedwood forests decreasing summer T mean from 0.23 to 0.72°C and increasing winter T min by up to 2°C, relative to non‐forested areas.Spatial predictions of temperatures from iButton data loggers were similar to a gridded climate product (ClimateNA), but the difference between them increased with potential solar radiation, vegetation cover, and terrain roughness.Species that can track their climate niche may be able to compensate for regional climate warming through local migrations to cooler microsites. Topographic and vegetation characteristics that are related to cooler local climates should be considered in the evaluation of future climate change impacts and to identify potential refugia from climate change.

Regional thermal analysis approach: A management tool for predicting water temperature metrics relevant for thermal fish habitat

The field of water resource management, including fisheries, is facing new challenges associated with climate change. This study sheds light on the modeling of water temperature indices (metrics) that describe critical thermal maxima of the Atlantic salmon (salmo salar). These thermal metrics include MaxWaterTmax (interannual mean of maximum summer temperature), MaxNumDay (interannual mean of the number of consecutive days with maximum water temperature > 25 °C and minimum water temperature > 20 °C). The latter is an important indicator to evaluate thermal variability. Three other parameters of a Gaussian function fitted to the interannual daily mean temperatures characterizing the thermal regime of 146 stations located in Eastern Canada were estimated. These three parameters are Gaussian_a (maximum of interannual daily mean temperature), Gaussian_b (mean duration of the warm period), and Gaussian_c (date of occurrence of the interannual maximum temperature). The classical Multiple linear regression model (MLR) and the non-linear Generalized additive model (GAM) were tested and compared to estimate the five thermal metrics. The regression-based approaches involve the identification of thermally homogeneous regions based on three approaches: hierarchical clustering analysis (HCA), regions of influence (ROI) as well as canonical correlation analysis (CCA). Then, the regional MLR and GAM models were applied within the delineated homogenous regions. Also, the regional models were compared to models encompassing all stations (i.e., one region). For each regional estimation model and each thermal metric, a set of optimal explanatory variables were selected using a forward stepwise procedure. The database consisted of 22 environmental predictors related to physiography, topography, climate, land cover and surface deposits. To assess performance of the models, the following statistical metrics were used: coefficient of determination R2, root mean square error (RMSE), bias, relative root mean square error (RRMSE) and percent bias (PBias). The results demonstrate that the non-linear GAM model was consistently better than the simpler MLR model for estimating the five thermal metrics. Results also show that the best practice consists in delineating homogeneous regions before applying the regional GAM model. According to all performance criteria, delineation of regions with the HCA approach is considered to be more flexible and to lead to better performances than neighborhood-based approaches (CCA and ROI).

Ensemble Climate and Streamflow Projections for the Assiniboine River Basin, Canada

The Assiniboine River Basin (ARB) is subject to an exceptionally variable precipitation regime of the Canadian Prairies, ranging between record droughts and unprecedented flooding in just the past decade. To assess the impact of a changing climate on hydroclimate variability in the ARB, we used the bias–corrected simulations from the Canadian Regional Climate Model (CanRCM4) to drive MESH model for two 60–year periods, a historical baseline (1951–2010) and future projection (2041–2100), under the Representative Concentration Pathway (RCP) 8.5 to simulate ARB flows at eight hydrometric stations. The precipitation is projected to increase in every season (~10–38%) except for summer (~−1–−5%). Minimum winter and maximum summer temperatures have the largest seasonal trends, increasing by 2–3 °C in the near future (2021–2050) and 5–6 °C in the far future (2051–2080). These climate changes produce higher winter river flows while peak runoff shifts by several weeks to earlier in the year. There is a shift in the magnitude and timing of extreme water levels. The ensemble of climate projections from a single model and one RCP to the variability and uncertainty in the future hydrology supports adaptation planning in the industrial sectors of Saskatchewan’s economy.