Search Results (39)

To build climate-resilient livestock systems, public policies must be informed by bioclimatic zoning, enabling region-specific interventions and more efficient resource allocation. This study aimed to conduct bioclimatic zoning for dairy cattle farming in the state of Maranhão, Brazil. Big data analysis techniques and predictive geostatistical modeling were applied to historical (2012–2023) and future climate scenarios under intermediate (RCP4.5) and high-intensity (RCP8.5) greenhouse gas emissions. Kriging maps of THI revealed a decreasing north–south thermal gradient, with values exceeding 80 during critical years. Milk yield losses were more pronounced in high-producing cows, reaching up to 5 kg/cow/day under extreme heat. Areas identified as drought-prone exhibited spatial patterns consistent with THI distributions. The projections indicate that, under the RCP 4.5 scenario, over 60% of Maranhão will exhibit average THI values between 78 and 81 by the end of the century. Under the RCP 8.5 scenario, large areas of the state are expected to reach THI values above 86. Under these conditions, estimated milk production losses may exceed 4 kg/cow/day for moderate-yielding animals and 9 kg/cow/day for high-yielding ones, respectively. The results reinforce the importance of bioclimatic zoning to support informed policymaking in the context of climate change. Full article

The study presents data on the role of lithological factors in the divergence of soil formation in forest–steppe and steppe ecosystems in a region of the East European Plain characterized by extremely contrasting geogenic conditions. Soils from different lithologic–geomorphologic combinations in the Samara region were chosen as the study object. It was shown that, in some cases, bioclimatogenic conditions are less decisive in the formation of the morphological organization and basic chemical parameters of the solum than the lithological characteristics of a particular locality. These lithological factors can transform soil morphology and affect the taxonomic position of soils at the subtype level and below. In landscapes marked by spatial and lithological contrasts at meso- and macro-levels, the use of a bioclimatic classification approach becomes inadequate, because it fails to highlight individual soil features. Thus, the development of lithological taxonomic and diagnostic criteria is necessary for the protection, proper use, and mapping of soils in complex geogenic, particularly lithological, conditions. Within one soil climatic zone, there can exist a large number of lithological soil subtypes, genera, and varieties. In such cases, the lithological framework has a stronger influence on soil spatial distribution than climatic gradients and associated vegetation ecotones. Full article

Understanding global patterns of tree canopy height and density is essential for effective forest management and conservation planning. This study examines how these attributes vary along latitudinal gradients and identifies key climatic drivers influencing them. We utilized high-resolution remote sensing datasets, including a 10 m resolution canopy height dataset aggregated to 1 km for computational efficiency, and a 1 km resolution tree density dataset derived from ground-based measurements. To quantify the relationships between forest structure and environmental factors, we applied nonlinear regression models and climate dependency analyses, incorporating bioclimatic variables from the WorldClim dataset. Our key finding is that latitude exerts a dominant but asymmetric control on tree height and density, with tropical regions exhibiting the strongest correlations. Tree height follows a quadratic latitudinal pattern, explaining 29.3% of global variation, but this relationship is most pronounced in the tropics (−10° to 10° latitude, R² = 91.3%), where warm and humid conditions promote taller forests. Importantly, this effect differs by hemisphere, with the Southern Hemisphere (R² = 67.1%) showing stronger latitudinal dependence than the Northern Hemisphere (R² = 35.3%), indicating climatic asymmetry in forest growth dynamics. Tree density exhibits a similar quadratic trend but with weaker global predictive power (R² = 7%); however, within the tropics, latitude explains 90.6% of tree density variation, underscoring strong environmental constraints in biodiverse ecosystems. Among climatic factors, isothermality (Bio 3) is identified as the strongest determinant of tree height (R² = 50.8%), suggesting that regions with stable temperature fluctuations foster taller forests. Tree density is most strongly influenced by the mean diurnal temperature range (Bio 2, R² = 36.3%), emphasizing the role of daily thermal variability in tree distribution. Precipitation-related factors (Bio 14 and Bio 19) moderately explain tree height (~33%) and tree density (~25%), reinforcing the role of moisture availability in structuring forests. This study advances forest ecology research by integrating high-resolution canopy structure data with robust climate-driven modeling, revealing previously undocumented hemispheric asymmetries and biome-specific climate dependencies. These findings improve global forest predictive models and offer new insights for conservation strategies, particularly in tropical regions vulnerable to climate change. Full article

The Balkan Peninsula is considered one of the most important centres of orchid diversity in Europe. However, the patterns of orchid species richness in the Central Balkans have not been sufficiently studied so far. The aim of this study was, therefore, to identify the centres of orchid diversity and the factors that influence the spatial variation in orchid species richness in the Central Balkans. For the analyses, the area of the Central Balkans was divided into 10 × 10 km grid cells. The environmental variables determined for each grid cell and used in the analyses were altitude, bioclimatic variables, geological substrates and habitat types. A random forest (RF) analysis was used to identify the environmental predictors most strongly associated with species richness. In addition to the total number of taxa, orchids with three belowground organ types were analysed separately: (a) rhizomatous orchids, (b) orchids with palmately lobed and fusiform tubers (“palmate tuberous orchids”) and (c) orchids with spherical or ovoid tubers (“ovoid tuberous orchids”). In the Central Balkans, 54 orchid species and subspecies have been recorded, and the most important centres of diversity are the Tara, Zvijezda, Jadovnik and Zlatar Mountains and the Ovčar-Kablar Gorge. In general, two groups of grid cells with the largest number of orchid taxa, i.e., hotspots, stood out: (1) grid cells with a large altitudinal range and (2) grid cells occupied by gorges and ravines. The most important gradients influencing orchid species richness are specific habitat types and altitudinal ranges, while climatic factors and geological substrates are less important. The most important factors affecting the richness of total and rhizomatous orchids are altitudinal range and habitat types (Abieti-Fagenion, Ostryo-Carpinion orientalis and Pinion nigrae forests), highlighting the important role of habitat heterogeneity. The maximum altitude, percentage of Abieti-Fagenion and Vaccinio-Picetea forests and the minimum value of the mean temperature of the driest quarter are the most important factors for determining the richness of palmate tuberous orchids, whereas the percentage of xero-thermophilous habitat types (Ostryo-Carpinion orientalis, Asplenietea trichomanis and Pinion nigrae) has the greatest influence on the richness of ovoid tuberous orchids. These results confirm the hypothesis concerning the origin and development of underground organs in orchids, emphasising that palmate tuberous orchids are best adapted to cold and humid habitat conditions, whereas ovoid tuberous orchids have the ability to grow in habitats with very warm and dry conditions. This study provides a good basis for better orchid conservation planning and underlines the importance of belowground strategies as a feature of orchid life history that should be considered when studying patterns of orchid diversity. Full article

The Andean paramos are unique and biodiverse environments. Located between the upper limit of forest and perpetual snow, they provide ecosystem services, especially freshwater supply; however, anthropogenic activities and climate change have altered their distribution and composition. This paper analyses the influence of altitude and climatic factors on the floristic composition of the páramos. A quasi-experimental study was used in three altitudinal gradients, collecting geolocalised data on species and bioclimatic variables. Principal component analysis, using the HJ-Biplot visualisation technique and k-means clustering algorithms, was applied to explore the relationships between factors. It was determined that the lower zone is nuanced by the presence of Solanaceae, which are important for human food. In the middle zone, there is a high diversity, with the Ericaceae and Caprifoliaceae families standing out, while in the upper zone, the Ericaceae and Gentianaceae families are accentuated. The PCA reveals that strata 2 and 3 share family taxa, while stratum 1 shows differences. Altitude and precipitation directly influence the distribution of species in each stratum; the Asteraceae family is dominant in the canton for its contribution to the principal components. Full article

The current study aims to analyze the fundamental ecological niches of five páramo species in the high Andean zones of South America. Hereby, the research focuses on the typical flora species of this ecosystem, such as Azorella aretioides, Azorella crenata, Azorella biloba, Azorella corymbosa, and Azorella pedunculata, testing whether their niches are more similar than expected. The study area in the Ecuadorian Andes encompasses altitudinal gradients between 2000 and 5000 m above sea level, where the species were previously registered. For the methodology, we used data from the Global Biodiversity Information Facility (GBIF) and identified the determining bioclimatic variables for the model as Bio1, Bio2, Bio3, Bio4, Bio12, Bio13, Bio14, Bio18, and Bio19. Moreover, we considered two alternative socioeconomic scenarios of climate change (SSP126 and SSP 245) projected for the period 2021–2040. In the same way, ecological niche models were evaluated and applied in order to identify areas of reduction in geographic space and the ecological space of the five species. Subsequently, the analysis revealed significant sensitivity of these high mountain species to variations in temperature and precipitation, with potential risks to associated species and water resources. In conclusion, changes in the fundamental niche were observed, with all species indicating reductions in distribution for 2040. This occurs particularly under the SSP245 scenario and especially in the equatorial zone, which currently exhibits extraordinary richness concerning the Azorella genus. Therefore, the study highlights evidently the vulnerability of páramo species to climate change, emphasizing the need for conservation efforts. Full article

This study provides pioneering research on the vegetation of archaeological areas in Iran to enhance its naturalistic and bioindication values by selecting the Pasargadae World Heritage Site (WHS). Vegetation surveys were carried out in different homogeneous habitats, analyzing the plant communities through statistical elaboration, syntaxonomic role, mapping, and enhancement of plants with conservation interest. In an ecological approach, the study included an analysis of the recent climate changes and human interventions influencing the water resources. Results revealed seven main vegetation types reflecting ecological gradients shaped by environmental, edaphic, and anthropogenic factors. The syntaxonomic analysis showed a primary subdivision in semi-natural grasslands and synanthropic vegetation. Several key species were identified as bioindicators of multiple factors, such as: Launaea acanthodes, Stipa barbata, Alhagi maurorum, Bellevalia saviczii, Glycyrrhiza glabra, Convolvulus arvensis, and Hordeum murinum. The vegetation map showed how the hilly grassland communities hosted the highest number of species with conservation interest and their need to be better protected. Bio-climatic data, such as the construction of dams and the exploitation for irrigation purposes, pointed to the increasing xeric conditions, which make urging conservation efforts for the site’s historical and naturalistic values. The study underscores the importance of preserving places with high plant diversity for effective site management, and enhances the intricate relationship between vegetation and natural features in the occurring environmental changes. Full article

This study employed an ensemble machine learning approach to evaluate the effect of bioclimatic covariates on the prediction accuracy of soil total carbon (TC) in the Pannonian biogeoregion. The analysis involved two main segments: (1) evaluation of base environmental covariates, including surface reflectance, phenology, and derived covariates, compared to the addition of bioclimatic covariates; and (2) assessment of three individual machine learning methods, including random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM), as well as their ensemble for soil TC prediction. Among the evaluated machine learning methods, the ensemble approach resulted in the highest prediction accuracy overall, outperforming the individual models. The ensemble method with bioclimatic covariates achieved an R² of 0.580 and an RMSE of 10.392, demonstrating its effectiveness in capturing complex relationships among environmental covariates. The results of this study suggest that the ensemble model consistently outperforms individual machine learning methods (RF, XGB, and SVM), and adding bioclimatic covariates improves the predictive performance of all methods. The study highlights the importance of integrating bioclimatic covariates when modeling environmental covariates and demonstrates the benefits of ensemble machine learning for the geospatial prediction of soil TC. Full article

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai’i Island using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, the support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied Gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m × 2 m M. polymorpha presence dataset and a 30 m × 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy. Full article

The geographical distribution of plants is influenced by macroclimate and dispersal limitations, which have led to lineage isolation and subsequent diversification within and across various environmental gradients. Macroclimatic variables in coastal wetlands influence plant species and lineages across biogeographical boundaries. This study aimed to determine the influence of macroclimatic variables on species and phylogenetic richness in South African estuaries. Open-source chloroplast DNA barcoding sequences, species distribution and climatic data layers were used to determine the relationship between species richness, MPD, MNTD and each bioclimatic variable individually. Temperate species richness and phylogenetic diversity were positively correlated with temperature bioclimatic variables whereas subtropical and tropical species were associated with increases in precipitation. Phylogenetic niche conservatism is evident in malvids and rosids which are restricted to tropical and subtropical regions due to their physiological adaptations to tropical climates. Caryophylales was mostly associated with temperate regions. Poales and Alismatales showed wide distributions that is likely attributed to traits related to wind pollination and hydrochory, rapid, clonal, and high reproductive output, tolerance to stressful conditions, and intraspecific genetic diversity. The findings highlight the importance of considering macroclimate and phylogenetic factors in understanding the distribution and diversity of coastal wetland plants. Full article

The impact of a changing climate, particularly global warming, often harms the distribution of pheasants, particularly those with limited endemic ranges. To effectively create plans of action aimed at conserving species facing threats such as the Western Tragopan, (Tragopan melanocephalus; Gray, 1829; Galliformes, found in the western Himalayas), it is crucial to understand how future distributions may be affected by anticipated climate change. This study utilized MaxEnt modeling to assess how suitable the habitat of the targeted species is likely to be under different climate scenarios. While similar studies have been conducted regionally, there has been no research on this particular endemic animal species found in the western Himalayas throughout the entire distribution range. The study utilized a total of 200 occurrence points; 19 bioclimatic, four anthropogenic, three topographic, and a vegetation variable were also used. To determine the most fitting model, species distribution modeling (SDM) was employed, and the MaxEnt calibration and optimization techniques were utilized. Data for projected climate scenarios of the 2050s and 2070s were obtained from SSPs 245 and SSPs 585. Among all the variables analyzed; aspect, precipitation of coldest quarter, mean diurnal range, enhanced vegetation index, precipitation of driest month, temperature seasonality, annual precipitation, human footprint, precipitation of driest quarter, and temperature annual range were recognized as the most influential drivers, in that order. The predicted scenarios had high accuracy values (AUC-ROC > 0.9). Based on the feedback provided by the inhabitants, it was observed that the livability of the selected species could potentially rise (between 3.7 to 13%) in all projected scenarios of climate change, because this species is relocating towards the northern regions of the elevation gradient, which is farther from the residential areas, and their habitats are shrinking. The suitable habitats of the Tragopan melanocephalus in the Himalayan region will move significantly by 725 m upwards, because of predicted climate change. However, the fact that the species is considered extinct in most areas and only found in small patches suggests that further research is required to avert a further population decline and delineate the reasons leading to the regional extinction of the species. The results of this study can serve as a foundation for devising conservation strategies for Tragopan melanocephalus under the changing climate and provide a framework for subsequent surveillance efforts aimed at protecting the species. Full article

Mt Etna in Sicily hosts a bryophyte floristic richness of 306 taxa, corresponding to 259 mosses, 43 liverworts, and 4 hornworts. Species richness shows a hump-shaped relationship with the elevation, with a peak at 1200–1700 m a.s.l. Chorotype patterns clearly change along an altitudinal gradient, from the Mediterranean, located at 0–300 m a.s.l., to Arctic-montane and boreo-Arctic montane at 1800–2700 m a.s.l., showing a correlation with the bioclimatic belts identified for the Mt Etna. In regard to the life form pattern, the turf species are the most represented in each elevation gradient, except at 2300–2700 m a.s.l. where the tuft species are prevalent. The life strategy pattern shows the colonists as the prevailing species, featured by an increasing trend up to 2200 m of elevation; above this limit, they are exceeded by the perennial stayers. Furthermore, taking into consideration the red-listed species (at the European and/or Italian level), as well as the species of phytogeographical interest, it was possible to identify the high bryophyte conservation priority areas; these areas are located in thermo-Mediterranean and oro-Mediterranean bioclimatic belts, the latter corresponding to the oldest substrates of the volcano where some of the most interesting bryophyte glacial relicts find refuge. Full article

Evapotranspiration demand has increased rapidly as temperatures have risen, affecting forest productivity. Consequently, carbon (C) uptake by forests is being modified; therefore, a more refined knowledge of the relationships between C capture and hydroclimate variability is required, particularly in drought-prone regions. In this study, we analyzed the relationships between climate and C capture as stemwood through the dendroecological analyses of radial growth in 15 conifer species distributed along a broad bioclimatic gradient in Mexico. The C content was calculated using densitometry data; correlations and mixed models were then used to determine the influence of climatic variables (precipitation, mean maximum and minimum temperatures, SPEI drought index) on tree growth. Each species showed specific responses to the climate with Taxodium mucronatum being the most responsive species. Both precipitation and maximum temperature best explained changes in C capture, with minimum temperature and SPEI playing secondary roles. The winter before the growth period was the most important season for C capture, particularly through positive responses to wet-cool conditions. However, the climatic influences of the current fall and summer were also notable. Seasonal climatic influences have implications for C uptake and forest productivity in the face of the severe droughts that repeatedly affect the study region. Full article

Philaenus spumarius and Neophilaenus campestris are the main vectors of the invasive bacteria Xylella fastidiosa and key threats to European plant health. Previous studies of the potential distribution of P. spumarius reveal that climatic factors are the main drivers of its distribution on the Mediterranean Basin scale. Other local studies reveal that the landscape could also have a role in the distribution of both species of P. spumarius and N. campestris. Our work is aimed at understanding the role and importance of bioclimatic and landscape environmental factors in the distributions of the vector and potential vector species P. spumarius, N. campestris, N. lineatus and L. coleoptrata on a regional scale across the Autonomous Community of Murcia (SE Spain), a region with relevant environmental gradients of thermality and crop intensity. We used sweeping nets for sampling 100 points during eight months in 2020. Using bioclimatic landscape composition and topographical variables, we carried out habitat suitability models for each species using the maximum entropy algorithm (MaxEnt). Distribution results for P. spumarius, N. campestris and N. lineatus indicate a gradient in habitat suitability, with the optimum in the coldest and wettest areas in landscapes with a high proportion of forest. All three species are absent from the southern third of the study region, the hottest, driest and most intensively cultivated area. These results are useful and should be considered in contingency plans against possible invasions of X. fastidiosa in Mediterranean regions. Full article

Morocco’s meteorological observation network is quite old, but the spatial coverage is insufficient to conduct studies over large areas, especially in mountainous regions, such as the Fez-Meknes region, where spatio-temporal variability in precipitation depends on altitude and exposure. The lack of station data is the main reason that led us to look for alternative solutions. TerraClimate (TC) reanalysis data were used to remedy this situation. However, reanalysis data are usually affected by a bias in the raw values. Bias correction methods generally involve a procedure in which a “transfer function” between the simulated and corrected variable is derived from the cumulative distribution functions (CDFs) of these variables. We explore the possibilities of using TC precipitation data for the Fez-Meknes administrative region (Morocco). This examination is of great interest for the region whose mountain peaks constitute the most important reservoir of water in the country, where TC data can overcome the difficulty of estimating precipitation in mountainous regions where the spatio-temporal variability is very high. Thus, we carried out the validation of TC data on stations belonging to plain and mountain topographic units and having different bioclimatic and topographic characteristics. Overall, the results demonstrate that the TC data capture the altitudinal gradient of precipitation and the average rainfall pattern, with a maximum in November and a minimum in July, which is a characteristic of the Mediterranean climate. However, we identified quasi-systematic biases, negative in mountainous regions and positive in lowland stations. In addition, summer precipitation is overestimated in mountain regions. It is considered that this bias comes from the imperfect representation of the physical processes of rainfall formation by the models. To reduce this bias, we applied the quantile mapping (QM) method. After correction using five QM variants, a significant improvement was observed for all stations and most months, except for May. Validation statistics for the five bias correction variants do not indicate the superiority of any particular method in terms of robustness. Indeed, results indicate that most QM methods lead to a significant improvement in TC data after monthly bias corrections. Full article