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Ticks are vectors of disease‐causing pathogens and are found on domestic and wild animals; thus, they are also vectors of significant human diseases. For this reason, pre‐emptive measures to prevent tick‐borne diseases are necessary in the form of exploring their major habitats, population increase period, and factors affecting their population growth, all of which indicate the purpose of this study. In the study, a variation of Haemaphysalis longicornis, a major vector of fever‐causing conditions, was statistically analyzed to identify the spatial and climatic factors affecting the time‐dependent variations of its population. The survey occurred in different habitats (grassland, mixed forest, deciduous forest, and coniferous forest) in South Korea. In addition, we fitted a phenology model by using a probability function to find the peak occurrence time annually. As a result, the numbers of adults and nymphs were found to be related to temperature and relative humidity and their population peaked at the end of May in all habitats except deciduous forests. This study is expected to provide information on habitat types, times, and climate patterns that require attention to help control H. longicornis populations. [ABSTRACT FROM AUTHOR]

The hydroelectric crisis is fomenting studies that seek the decentralization of electric energy generation in Brazil. Due to the climatic dependence on alternative sources, knowing their intermittent behavior is the best way to ensure supply. This article presents an analysis of energy complementarity based on the occurrences of climate variables with precipitation for hydroelectric power, irradiation for photovoltaic, and wind speed for wind power. We aimed to analyze and spatialize the availability of renewable energy sources in western Paraná through historical series (2011 to 2020), statistical analysis (monthly and seasonal), and the use of Geographic Information System (GIS). Daily data records were made available by the Paraná Meteorological Institute (SIMEPAR), referring to eight weather stations throughout the mesoregion. The results identified spatial complementarity for the mesoregion West Paraná. Cascavel city stood out for temporal complementarity, recording the highest values for precipitation in spring and wind speed in winter. In addition, irradiation data excelled in summer for the city of Santa Helena when compared to the other municipalities considered. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) as a component in the hydrological cycle is calculated using many methods. In this study, the capability of four data‐driven methods including artificial neural network (ANN), adaptive neuro‐fuzzy inference system (ANFIS), support vector machine (SVM), and M5 tree model has been evaluated for daily ETo estimation at the south of the Caspian Sea. Different combinations of climatic data, solar radiation (Rs), mean air temperature (Tmean), mean relative humidity (RHmean), and wind speed (U) during 1991–2020 were used as input variables. The data were divided into training and test data. The values generated from the methods were compared with those of the FAO‐56 Penman‐Monteith as a standard method. The results indicated that the accuracy of ANFIS was increased for estimating ETo, especially in validation phase, when all climatic variables were used as inputs in the synoptic stations. Totally, based on the evaluation of the performances, it was created that the ANFIS with Tmean, RHmean, Rs, and U variables had the best accuracy, while the ANN, SVM, and M5 with only one input of U had the worst performance. The ANFIS with Tmean and Rs was recommended for modelling ETo if there are fewer climatic variables in these regions. [ABSTRACT FROM AUTHOR]

Deadwood is an important component of the global carbon cycle, and its decomposition releases carbon dioxide (CO2) and methane (CH4) into the atmosphere. However, the main drivers of these greenhouse gas emissions from deadwood are not well understood. We investigated drivers that govern the CO2 and CH4 emission rates of 793 deadwood specimens from 13 different tree species, which were exposed on 27 forest and 38 grassland plots at Schorfheide‐Chorin (Germany) for one year. Tree species identity was an important driver for emissions of both gases, whereas habitat type and management intensity were only important for CO2 emission rate. CO2 emission rates were positively linked to mass loss and were one‐third higher in forest compared to grassland habitats. The wood traits organic extractives, lignin, and sulfur content were negatively associated with CO2 emission rates, whereas carbon, nitrogen, and magnesium content showed the opposite effect. Among climate variables, air humidity in forest and soil moisture in grassland habitats positively affected CO2 emission rates. CH4 emission rates showed a negative relationship with increasing wood density exposed in both habitat types but were positively related to tree species with higher sulfur contents. Taken together, CO2 emission rates from deadwood were well predicted by wood traits, management intensity and climatic variables, whereas CH4 emission rates were less well predictable and were influenced only by wood traits that differed from those of CO2 emissions. Our results provide a deeper insight into the mineralization processes of deadwood and should be considered in further carbon cycle assessments. Plain Language Summary: Living trees are an essential reservoir for carbon storage. While the process of photosynthesis describes the ability of plants to fix atmospheric carbon, the decomposition of deadwood is the first stage of recycling this nutrient. Aerobic decomposition of deadwood is mainly carried out by specialized fungi that release carbon into the atmosphere during their respiration processes. Most microorganisms require oxygen to utilize deadwood and form carbon dioxide during respiration. In addition, archaea can respire carbon dioxide in the absence of oxygen, which leads to methane production. In our study, we determined the gas emission rates of carbon dioxide and methane for 13 tree species and investigated the influence of regional climate and habitat conditions and types (grassland and forest). The emission rates of carbon dioxide and methane largely differed among tree species, which probably depended on their wood traits. Both gas emission rates were higher under humidity, while higher temperatures increased only carbon dioxide emission rates. Methane emission rates were independent of habitat conditions, while carbon dioxide in forest habitats were higher and influenced by naturally existing tree species. Our results improve future assessment of the impact of deadwood on the global carbon cycle. Key Points: Tree species identity is an important driver of CO2 and CH4 emission ratesCO2 emission is two‐thirds higher in forest compared to grassland habitatsForest management intensity decreased CO2 emission rates, whereas CH4 was only affected by single management components [ABSTRACT FROM AUTHOR]

There are limited literature on the impacts of drought on crop yields in warm regions such as southwest China. Drought vulnerability of four different crops (wheat, rice, maize and sugarcane) cultivated in three provinces (Sichuan, Guizhou and Yunnan) within southwest China were investigated in this study. It was based on the drought index of standardized precipitation evapotranspiration index (SPEI) for ‐3‐ and ‐6 months timescales (SPEI‐3 and SPEI‐6). The correlation between the SPEI and the standardized yield residuals series index for the individual crops was estimated for the period from 1960 to 2018. The highest drought duration was recorded in the southern part of the study area especially in the Yunnan Province. For SPEI‐3, 60% of the total area was affected by drought mainly during the months from August to December for about 13 years (2005–2018). In terms of SPEI‐6, the total affected area by the drought exceeded 80% during the timeframe from 2009 to 2013. Among the studied crops, winter wheat had the highest annual crop yield losses particularly in 2010 when the loss exceeded 50%. The results of this study have implications for agricultural management and climate policymaking in minimizing the influence of drought under the warming climate in southwest China. Further, it provides greater insight into crop–climate interactions and sustainable crop production. [ABSTRACT FROM AUTHOR]

Aim: Stemflow has been increasingly recognized as an indispensable component in water and nutrient budgets within vegetated ecosystems. Here we aim to quantify the stemflow percentage (St, %) of incident precipitation (i.e. stemflow production) at a global scale, and to provide a systematic evaluation on how biotic and abiotic factors affect St. Location: Global. Time period: 1970–2019. Major taxa studied: Woody plants (trees and shrubs). Methods: We compiled a global stemflow dataset from 234 peer‐reviewed papers, which included 488 observations of St and the related biotic (stand characteristics) and abiotic factors (climate variables) at 283 sites within terrestrial woody plant ecosystems. We explored the global pattern of St and performed a machine learning method (boosted regression trees) to model the effects of biotic and abiotic variables on St. Results: Globally, the median (interquartile range, IQR) St was 2.7% (1.0–6.3%). We found that St in arid zones (type B in the Köppen‐Geiger climate classification) was significantly higher (p <.01) than in other climate types, and we also detected a significant difference (p <.01) in St between trees (median: 2.4%; IQR: 1.0–5.3%) and shrubs (median: 7.2%; IQR: 5.2–11.9%). Predictor variables that substantially accounted for the explained deviance of the final model included vegetation height (27.0%), mean annual precipitation (16.1%), mean annual temperature (14.4%), stand density (10.8%), stand age (8.9%) and bark type (5.5%). In contrast, leaf area index, diameter at breast height, basal area, phenology type, life‐form and leaf type were classified as low importance. Main conclusions: Our synthesis provides a cross‐site comparison of St, and gives a holistic view on how climate variables and stand characteristics contribute to and affect global stemflow production. [ABSTRACT FROM AUTHOR]

Analysing dryness/wetness trends, which is typically achieved by examining drought indices, is an essential approach for assessing extreme climate events. However, since there are differences among studies in terms of selected climate variables and timescales used to calculate drought indices, there is uncertainty regarding the magnitude and direction of the dryness/wetness, trend even for the same region. We used 40 years weather station data (1970–2010) and studied the uncertainties of dryness/wetness trends in conterminous China. The effects of selected climate variables, timescales and their interaction on the dryness/wetness trends were quantified by comparing the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index (SPEI) with the Thornthwaite equation and SPEI with the Penman–Monteith equation at 3‐, 12‐ and 24‐month timescales. The results indicated that (a) higher uncertainties occurred in relatively arid and humid regions; (b) timescale tended to exaggerate the effect of climate variables, even though its effect was substantially lower than climate variables; (c) relative humidity and precipitation were the primary climate variables affecting observed dryness/wetness trend uncertainties. Since the key climate variables changed depending on the selected timescale, the role of timescale should be further examined the Inner Mongolia and Northeast regions. In general, the study results suggest that the trend analysis of dryness/wetness should include the interaction of both climate variable and timescale selection, particularly when a single drought index is used to estimate the dryness/wetness trend in diverse regions. [ABSTRACT FROM AUTHOR]

Soya bean's growing area has increased in Hungary since the 2000s, yet few direct field‐based evapotranspiration measurements are available. Compensation evapotranspirometer was used to control two watering levels (unlimited and water stress limited to flowering) at Hungary (45°N), in a three‐season study (2017–2019). Two soya bean varieties with different water demands, Sinara and Sigalia, were analysed. During these vegetation periods, the total evapotranspiration in unlimited water level (both varieties) was above 600 mm, while water stress during flowering reduced it to about half. Water losses in the wet 2018 were slightly lower than during the dry 2017 and 2019. Correlation coefficient values between evapotranspiration and weather variables implied that water losses were controlled by available energy and relative humidity in dry and wet seasons, respectively. Using easily accessible meteorological variables, the air temperature was found to be the best estimator of evapotranspiration. Due to water stress under flowering, the seasonal mean yields varied irregularly in both varieties. Irrespective to season, the water productivity for grain showed a trend to lower values in unlimited watering compared to water stress conditions. Crop‐water response in both soya bean varieties would offer valuable information for breeders and seed distributors in identifying better varieties under mild continental climate. [ABSTRACT FROM AUTHOR]

Aim: Among the numerous anthropogenic pressures threatening biodiversity, habitat destruction and climate change are pointed to as dominant. In response, a number of mitigation strategies are elaborated to save endangered living organisms. However, the taxonomic level and geographical extent at which conservation strategies should be designed and implemented remain generally unclear. Here, we aim to assess and discuss the importance to apply conservation strategies at an appropriate taxonomic scale. For this purpose, we focus our analyses on bumblebees (genus Bombus), a group of critically important and endangered pollinators. Location: West‐Palaearctic. Methods: We use a species distribution modelling approach to investigate and compare climatic and habitat‐related variables associated with the distribution of West‐Palaearctic bumblebees. Our analyses are based on a data set gathering more than 125,000 unique observation points for 68 species. Results: We highlight species‐specific associations with climatic and land cover variables, depicting the strong relevance of taxon‐specific mitigation strategies for the conservation of those key pollinators. We also identify that the occurrence probability of localized and widespread species is mostly predicted by specific land cover characteristics and climatic conditions, respectively. Finally, we report the general absence of phylogenetic signal associated with the relative importance of each environmental variable in species distribution models, underlining the difficulty to predict species‐specific environmental requirements based on evolutionary relationships. Main conclusions: In the light of these results, we conclude that climate change and landscape destruction are not expected to drive the fate of all bumblebee species in a same direction, even for phylogenetically close lineages. We argue in favour of geographically and taxonomically adapted conservation strategies and discuss the limitations of untargeted action plans for species with different climatic/habitat requirements. [ABSTRACT FROM AUTHOR]

The Qinghai–Tibet Plateau (QTP) is particularly sensitive to global climate change, especially to elevated temperatures, when compared with other ecosystems. However, few studies use long‐term field measurements to explore the interannual variations in plant biomass under climate fluctuations. Here, we examine the interannual variations of plant biomass within two vegetation types (alpine meadow and alpine shrub) during 2008–2017 and their relationships with climate variables. The following results were obtained. The aboveground biomass (AGB) and belowground biomass (BGB) response differently to climate fluctuations, the AGB in KPM was dominated by mean annual precipitation (MAP), whereas the AGB in PFS was controlled by mean annual air temperature (MAT). However, the BGB of both KPM and PFS was only weakly affected by climate variables, suggesting that the BGB in alpine ecosystems may remain as a stable carbon stock even under future global climate change. Furthermore, the AGB in PFS was significantly higher than KPM, while the BGB and R/S in KPM were significantly higher than PFS, reflecting the KPM be more likely to allocate more photosynthates to roots. Interestingly, the proportion of 0–10 cm root biomass increased in KPM and PFS, whereas the other proportions both decreased, reflecting a shift in biomass toward the surface layer. Our results could provide a new sight for the prediction how alpine ecosystem response to future climate change. [ABSTRACT FROM AUTHOR]

Summary: African swine fever (ASF) is a transcontinental, contagious, fatal virus disease of pig with devastating socioeconomic impacts. Interaction between infected wild boar and domestic pig may spread the virus. The disease is spreading fast from the west of Eurasia towards ASF‐free China. Consequently, prediction of the distribution of ASF along the Sino‐Russian‐Korean borders is urgent. Our area of interest is Northeast China. The reported ASF‐locations in 11 contiguous countries from the Baltic to the Russian Federation were extracted from the archive of the World Organization for Animal Health from July 19, 2007 to March 27, 2017. The locational records of the wild boar were obtained from literature. The environmental predictor variables were downloaded from the WorldClim website. Spatial rarefication and pair‐wise geographic distance comparison were applied to minimize spatial autocorrelation of presence points. Principal component analysis (PCA) was used to minimize multi‐collinearity among predictor variables. We selected the maximum entropy algorithm for spatial modelling of ASF and wild boar separately, combined the wild boar prediction with the domestic pig census in a single map of suids and overlaid the ASF with the suids map. The accuracy of the models was assessed by the AUC. PCA delivered five components accounting for 95.7% of the variance. Spatial autocorrelation was shown to be insignificant for both ASF and wild boar records. The spatial models showed high mean AUC (0.92 and 0.97) combined with low standard deviations (0.003 and 0.006) for ASF and wild boar, respectively. The overlay of the ASF and suids maps suggests that a relatively short sector of the Sino‐Russian border has a high probability entry point of ASF at current conditions. Two sectors of the Sino‐Korean border present an elevated risk. [ABSTRACT FROM AUTHOR]

Aim: We use cluster analysis to delimit climatically and functionally distinct mammalian faunal clusters. These entities form regional species pools and are relevant to community assembly processes. Similar clusters can be differentiated in the fossil record, offering the potential for use as palaeoenvironmental proxies. Location: North America within W178°, W14°, N83°, N7° and Europe within W32°, E35°, N80°, N35°. Major taxa studied: 575 and 124 land mammal species from North America and Europe. Methods: K‐means clustering was used to subdivide North America and Europe into distinct faunas ranging in number from 3 (largest scale) to 21 (smallest scale). Each set of faunas was tested for significant differences in climate (mean annual precipitation, mean annual temperature) and functional traits (body mass, locomotion and diet). Results: In North America, climatic differentiation exists at the scale where mammals are divided into 11 or fewer distinct faunas and, in Europe, at the scale where there are five or fewer faunas. Functional trait differentiation in body mass occurs at a larger spatial scale in North America (8 distinct faunas), but locomotor differentiation is present at all spatial scales, and dietary differentiation is not present at any scale. No significant differentiation in any functional trait at any scale was found in Europe. Main conclusions: Faunal clusters can be constructed at any spatial scale, but clusters are climatically and functionally meaningful only at larger scales. Climatic (and environmental) differences and their associated functional trait specialisations are likely to be barriers to large‐scale mixing. We argue, therefore, that functionally and climatically distinct faunal clusters are the entities that form regional species pools for community assembly processes. In North America, there are eight such mammal pools, but only one in Europe. Since the functional traits in our study are observable in the fossil record, functional trait analysis can potentially be used to diagnose climatically distinct regions in the past. [ABSTRACT FROM AUTHOR]

The delineation of unique areas in biodiversity is crucial for conservation planning. However, identification of distinct biotas is a challenging task. In this study, we use an objective analytical process to test if bird and plant species composition of three different forest formation in the Atlantic forest Biome is sufficiently distinct to merit the recognition of ecoregions. We also evaluated how current climatic and spatial factors shape the distribution of plants and birds. Sampling localities were selected in three forest types of southern Brazil: mixed rain forest (MF), seasonal semi‐deciduous forest (SF) and dense rain forest (DF). The point‐centred quarter method and point counts were used to sample plants and birds respectively. Multivariate analyses were used to evaluate species composition, while the Rank Occupancy Abundance Profile was used to determine the association of bird species with each forest type. We used variation partitioning to determine which factors, that is climatic, spatial or tree species abundance (in the case of birds) better explained the distribution of plants and birds. A total of 176 tree species and 270 bird species were recorded. The bird species composition was sufficiently distinct among the three forest types, but the plant species composition was only distinct between DF and MF. Moreover, 61% of bird species were associated with only one forest type. This high association and the different species composition suggest that these forest types are sufficiently distinct to merit the recognition of ecoregions. Plant distribution was mainly affected by climatic variables, while bird distribution was mainly influenced by forest type and less so by climatic variables. Our data suggest that all three forest types should be conserved, but conservation strategies are urgent in MF where only 0.39% of its original cover is under protection. [ABSTRACT FROM AUTHOR]

Abstract: Ectothermic vertebrates are sensitive to thermal fluctuations in the environments where they occur. To buffer these fluctuations, ectotherms use different strategies, including the integument, which is a barrier that minimizes temperature exchange between the inner body and the surrounding air. In lizards, this barrier is constituted by keratinized scales of variable size, shape and texture, and its main function is protection, water loss avoidance and thermoregulation. The size of scales in lizards has been proposed to vary in relation to climatic gradients; however, it has also been observed that in some groups of Iguanian lizards it could be related to phylogeny. Thus, here, we studied the area and number of scales (dorsal and ventral) of 60 species of Liolaemus lizards distributed in a broad latitudinal and altitudinal gradient to determine the nature of the variation of the scales with climate, and found that the number and size of scales are related to climatic variables, such as temperature and geographical variables as altitude. The evolutionary process that best explained how these morphological variables evolved was the Ornstein‐Uhlenbeck model. The number of scales seemed to be related to common ancestry, whereas dorsal and ventral scale areas seemed to vary as a consequence of ecological traits. In fact, the ventral area is less exposed to climate conditions such as ultraviolet radiation or wind and is, thus, under less pressure to change in response to alterations in external conditions. It is possible that scale ornamentation, such as keels and granulosity, may bring some more information in this regard. [ABSTRACT FROM AUTHOR]

Abstract: Identification of climatic drivers that simulate the variability of year to multiyear sustained rainfall anomalies is important for water resource management. This study provides a comprehensive investigation of the relationship of drought and climatic variables filtered to represent specific frequency bands using a wavelet transform. The Standardized Precipitation Index (SPI) is used to represent drought periods and wet anomalies over Australia. Strong evidence is found that the year to multiyear SPI extremes are strongly related to the low‐frequency signal present in climate variables. Interannual variability (period of 3.73 and 7.4 years) of climate variables are associated with drought at the annual scale, whereas multiyear droughts are strongly influenced by the variability at interdecadal frequencies (period of 14.9 years). The strength of these relationships is found to vary with climate variables and regions. While significant negative correlation between SPI and surface air temperature at interannual and interdecadal frequencies exists over the eastern Australia, this relationship is positive for parts of Western Australia. Regional analysis for 13 river basins over Australia indicates that wavelet decomposed low frequencies of climate variables perform better at predicting drought and wet periods than the unfiltered climate variables. Identifying filtered climatic predictors has significant potential in reconstructing drought records in past climates as well as simulating likely drought for future climates. Additionally, the selection of filtered climatic predictor variables enables an assessment of the improvements needed in climate model simulations so as to improve our ability to better simulate drought or sustained wet extremes. [ABSTRACT FROM AUTHOR]

Polyploidisation has played an important role in plant diversification, and variation in ploidy level may be found not only between species of the same genus, but also within a single species. Although establishing the adaptive significance of polyploidy to explain the geographic distribution of cytotypes is challenging, the occurrence of different cytotypes in different ecological niches may suggest an adaptive role of genome duplication., We studied the adaptive significance of the geographic distribution of cytotypes across the entire distribution range of the endemic Erysimum mediohispanicum (Brassicaceae). For that, we have used climate variables, population elevation and soil properties to model ecological niches for the different cytotypes. In addition, we analysed the effect that ploidy level has on the floral phenotype., We found a clear geographic pattern in the distribution of cytotypes, with diploid individuals occurring in the southernmost part of the distribution range, while tetraploids were found in the northern area. A contact (mosaic) zone between both cytotypes was identified, but diploids and tetraploids occur in sympatry in only one population (although in a highly unbalanced proportion). Gene flow between different cytotypes seems to be negligible, as evident from an almost complete absence of triploids and other minority cytotypes. Niches occupied by both cytotypes showed subtle, but significant differences, even in the contact zone. Precipitation was higher in regions occupied by tetraploid individuals, which present wider corolla tubes and thinner but taller stalks than diploids., Our findings highlight the potential role of polyploidy in the ecological adaptation of E. mediohispanicum to both abiotic factors and biotic interactions. [ABSTRACT FROM AUTHOR]

Aim Niche-based species distribution models (SDMs) are commonly used to predict impacts of global change on biodiversity, but the reliability of these predictions in space and time depends on their transferability. We tested how the strategy used to choose predictors impacts the transferability of SDMs at a cross-continental scale. Location North America, Eurasia and Australia. Method We used a systematic approach including 50 Holarctic plant invaders and 27 initial predictor variables, considering 10 different strategies for variable selection, accounting for the proximality, multicollinearity and climate analogy of predictors. We compared the average performance of each strategy, some of which used a large number of predictor combinations. Next, we looked for the single best model for each species across all the predictor combinations retained in the analysis. Transferability was considered as the predictive success of SDMs calibrated in the native range and projected onto the invaded range. Results Two strategies showed better SDM transferability on average: a set of predictors known for their ecologically meaningful effects on plant distribution, and the two first axes of a principal component analysis calibrated on all predictor variables (Spc2). From the more than 2000 combinations of predictors per species across strategies, the best set of predictors yielded SDMs with good transferability for 45 species (90%). These best combinations consisted of eight randomly assembled (39 species) or uncorrelated predictors (6 species) and Spc2 (5 species). We also found that internal cross-validation was not sufficient to give full information about the transferability of a SDM to a distinct range. Main conclusion Transferring SDMs at the macroclimatic scale, and thus anticipating invasions, is possible for the large majority of invasive plants considered in this study, but the accuracy of the predictions relies strongly on the choice of predictors. From our results, we recommend including either proximal and state-of-the-art variables or a reduced and orthogonalized set to obtain robust SDM projections. [ABSTRACT FROM AUTHOR]

Aim: Soil microorganisms play key roles in regulating many important ecosystem processes. However, our understanding of the patterns and drivers of soil microbial communities remains limited. This study aims to test the hypothesis that edaphic factors are more important in explaining variations in soil microbial communities than climatic and biotic factors, as soils directly provide substrates and environment for soil microbial communities. Location: Tibetan alpine grasslands and global terrestrial biomes. Methods: On the basis of phospholipid fatty acid (PLFA) analysis, we investigated large-scale patterns and drivers of soil microbial communities using data obtained from 196 sites within two major grassland types (alpine steppe and alpine meadow) on the Tibetan Plateau. We also explored global patterns of soil microbial communities by analysing published data from around the world. Results: Soil microbial PLFAs in Tibetan alpine grasslands were positively associated with mean annual temperature and mean annual precipitation (MAP), contradicting patterns previously observed across global biomes. A combined stepwise regression and variation partitioning analysis revealed that soil microbial community variations in Tibetan alpine grasslands were mainly explained by edaphic factors, such as soil organic carbon, C : N ratio, pH and soil texture, then by biotic factors, such as aboveground biomass and plant species richness, and further by climatic factors, such as MAP. The global analysis confirmed that edaphic factors accounted for a greater portion of the variation in soil microbial communities than did climatic and biotic variables. Main conclusions: These results provide strong support for the hypothesis that edaphic factors are the dominant drivers of spatial variations in soil microbial communities across regional and global scales. [ABSTRACT FROM AUTHOR]

Photosynthetic capacity, determined by light harvesting and carboxylation reactions, is a key plant trait that determines the rate of photosynthesis; however, in Earth System Models (ESMs) at a reference temperature, it is either a fixed value for a given plant functional type or derived from a linear function of leaf nitrogen content. In this study, we conducted a comprehensive analysis that considered correlations of environmental factors with photosynthetic capacity as determined by maximum carboxylation (Vc,m) rate scaled to 25°C (i.e., Vc,25; μmol CO2·m-2·s-1) and maximum electron transport rate (Jmax) scaled to 25°C (i.e., J25; μmol electron·m-2·s-1) at the global scale. Our results showed that the percentage of variation in observed Vc,25 and J25 explained jointly by the environmental factors (i.e., day length, radiation, temperature, and humidity) were 2-2.5 times and 6-9 times of that explained by area-based leaf nitrogen content, respectively. Environmental factors influenced photosynthetic capacity mainly through photosynthetic nitrogen use efficiency, rather than through leaf nitrogen content. The combination of leaf nitrogen content and environmental factors was able to explain ~56% and ~66% of the variation in Vc,25 and J25 at the global scale, respectively. Our analyses suggest that model projections of plant photosynthetic capacity and hence land-atmosphere exchange under changing climatic conditions could be substantially improved if environmental factors are incorporated into algorithms used to parameterize photosynthetic capacity in ESMs. [ABSTRACT FROM AUTHOR]

It is increasingly common for studies of animal ecology to use model‐based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non‐snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (<1 km2) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population‐level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km2) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model's resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables. [ABSTRACT FROM AUTHOR]

Aim To produce a spatial clustering of Europe on the basis of species occurrence data for the land mammal fauna. Location Europe defined by the following boundaries: 11°W, 32°E, 71°N, 35°N. Methods Presence/absence records of mammal species collected by the Societas Europaea Mammalogica with a resolution of 50 × 50 km were used in the analysis. After pre-processing, the data provide information on 124 species in 2183 grid cells. The data were clustered using the k-means and probabilistic expectation maximization (EM) clustering algorithms. The resulting geographical pattern of clusters was compared against climate variables and against an environmental stratification of Europe based on climate, geomorphology and soil characteristics (EnS). Results The mammalian presence/absence data divide naturally into clusters, which are highly connected spatially and most strongly determined by the small mammals with the highest grid cell incidence. The clusters reflect major physiographic and environmental features and differ significantly in the values of basic climate variables. The geographical pattern is a fair match for the EnS stratification and is robust between non-overlapping subsets of the data, such as trophic groups. Main conclusions The pattern of clusters is regarded as reflecting the spatial expression of biologically distinct, metacommunity-like entities influenced by deterministic forces ultimately related to the physical environment. Small mammals give the most spatially coherent clusters of any subgroup, while large mammals show stronger relationships to climate variables. The spatial pattern is mainly due to small mammals with high grid cell incidence and is robust to noise from other subsets. The results support the use of spatially resolved environmental reconstructions based on fossil mammal data, especially when based on species with the highest incidence. [ABSTRACT FROM AUTHOR]

Abstract: Relative abundances of tree species are presented for the 348 forest plots described in E. Lucy Braun's (1950) book, Deciduous Forests of Eastern North America (Hafner, New York, facsimile reprint 1972). Information about the plots includes forest type, location with latitude and longitude, WorldClim climate variables, and sources of original studies where applicable. No copyright restrictions are associated with the use of this data set. Please cite this article when the data are used in other publications. [ABSTRACT FROM AUTHOR]