Patterns Of Spatial Variation Research Articles

Spatiotemporal evolution patterns and underlying formation mechanisms of monsoon rainfall across eastern China: A complex network perspective

Quantifying the evolution patterns and forming causes of monsoon rainfall is essential for prediction and mitigation of resultant hydrometeorological hazards. This study proposed a complex network-based framework to investigate these aspects over eastern China throughout the monsoon season. Based on the extracted heavy precipitation events (HPEs) at each grid within the monsoon season and twenty segmented phases, event synchronization and complex networks were utilized to construct HPE synchronized networks and HPE delayed networks, respectively. The former were utilized to identify monsoon rainfall subregions over eastern China, and further investigate the evolution rules of monsoon rainfall and associated hazard risks across the monsoon season. The latter were employed to investigate the spatial propagation patterns of monsoon rainfall within eastern China and individual subregions during their respective rainy seasons. Furthermore, the driving mechanisms behind the spatial propagation of monsoon rainfall within various subregions were explored by diagnosing the typical spatial propagation periods of monsoon rainfall and relevant regional atmospheric circulation anomalies. Results show that there are ten monsoon rainfall subregions over eastern China. Across these subregions, the monsoon rainfall undergoes seven sequential stages, including three stationary periods, two abrupt northward jumps and one southward retreat. As phases progress, zones with high hazard risks migrate within South China (SC), Yangtze River Basin (YRB), and Liaohe River Basin (LRB) with area first increasing and then decreasing from mid-April to early August. Diverse spatial propagation patterns of monsoon rainfall within various subregions arise from variations in the surrounding atmospheric circulation systems, resulting in anomalous wind patterns and moisture distribution. These anomalies, in turn, foster favorable conditions for monsoon rainfall to propagate along anomalous water vapor transport pathways. These findings advance our understanding of monsoon systems and associated hazard risks, shedding new light on their predictability.

Evolution and Optimization Simulation of Coastal Chemical Industry Layout: A Case Study of Jiangsu Province, China

In the face of geopolitical challenges and climate change, economic progress, safe production, and environmental protection have emerged as important directions for chemical industry development. However, the rational optimization of the chemical industry layout under the backdrop of ecological environmental protection necessitates further exploration. This study explores the evolution and future development direction of the chemical industry layout within the coastal region of Jiangsu Province, China, using the CA–Markov model. The findings reveal a trend of spatial agglomeration growth among coastal chemical enterprises, with Moran’s Index increasing from 0.109 in 2007 to 0.206 in 2017. The petrochemical industry, in particular, demonstrated the most significant agglomeration effect, with approximately 52.10% being concentrated in 14 coastal industrial parks in 2017. Under the constraints of the ecological environment and policy guidance, the land area allocated for the chemical industry experienced a reduction of over 10%, further strengthening the emphasis on spatial agglomeration. Chemical industries along Jiangsu’s coast have become agglomerated and concentrated near industrial parks and ports. Their spatial distribution and connectivity were mainly influenced by factors such as convenient transportation, the ecological environment, local policies, the distance from residential areas, and industrial agglomeration. Under different scenarios—including natural growth, ecological environment constraints, and policy guidance—chemical industries show diverse spatial patterns. Ecological environmental constraints and policy guidance can provide various intervention methods for the government to promote the optimization direction and focus of the chemical industry layout while minimizing its impact on the ecological environment.

Remotely sensed seasonal dynamics of an estuarine plume in the coastal zone of El Rincón system (Southwestern Atlantic Ocean)

In this work we analyzed 18 years (2003–2021) of MODIS-Aqua daily images and multi-annual monthly mean turbidity maps to provide a comprehensive characterization of seasonal patterns of turbidity in the transitional area between the mouth of the Bahía Blanca Estuary and the adjacent inner shelf (El Rincón coastal system, Argentina). Field measurements collected in the study area were combined with MODIS data processed under different schemes of atmospheric correction before turbidity retrieval. Then, an Empirical Orthogonal function (EOF) analysis was used to assess the temporal patterns of turbidity and their regional variability over the inner shelf. Through a Generalized Linear Model (GLM), changes in the turbidity plume were further associated with environmental forcing (i.e. tides, winds and precipitation) controlling the seasonal dynamics. A strong gradient of decreasing turbidity from the northeast (close to the estuarine region) to the southwest (coastal region) was detected, while a major spatial pattern of annual variability, characterized by decreasing seasonality from northwest to southeast was defined. According to our results, the frequency of northwest wind direction and the impact of the wind mixing are considered as primary factors that shape the seasonal variability of turbidity with a strong winter dominance of largest plume events. The results found in this study are key to understanding the general dynamics of biological activity in the regional waters and the implications that climate variability might have on this complex ecosystem.

Spatial and temporal analysis of haemorrhagic septicaemia outbreaks in India over three decades (1987–2016)

Haemorrhagic septicaemia (HS) is an economically important disease affecting cattle and buffaloes and the livelihoods of small-holder farmers that depend upon them. The disease is caused by Gram-negative bacterium, Pasteurella multocida, and is considered to be endemic in many states of India with more than 25,000 outbreaks in the past three decades. Currently, there is no national policy for control of HS in India. In this study, we analysed thirty year (1987–2016) monthly data on HS outbreaks using different statistical and mathematical methods to identify spatial variability and temporal patterns (seasonality, periodicity). There was zonal variation in the trend and seasonality of HS outbreaks. Overall, South zone reported maximum proportion of the outbreaks (70.2%), followed by East zone (7.2%), Central zone (6.4%), North zone (5.6%), West zone (5.5%) and North-East zone (4.9%). Annual state level analysis indicated that the reporting of HS outbreaks started at different years independently and there was no apparent transmission between the states. The results of the current study are useful for the policy makers to design national control programme on HS in India and implement state specific strategies. Further, our study and strategies could aid in implementation of similar approaches in HS endemic tropical countries around the world.

Quantifying the Spatial Distribution Pattern of Soil Diversity in Southern Xinjiang and Its Influencing Factors

Soil diversity plays an important role in maintaining ecological balance and ensuring the sustainability of the land. Xinjiang is a typical arid and semi-arid region of China, and the study of Xinjiang soils is significant for understanding soil properties in all such environments. This study applied the moving window technique and the species–area curve model from ecology to establish optimal analysis windows, calculate landscape pattern indices, and reveal soil distribution characteristics in Southern Xinjiang. Additionally, we used geographic detectors to identify the primary influencing factors in different geomorphic regions. The results indicate a positive correlation between soil richness and area in the Southern Xinjiang region. The Tarim Basin, despite being the largest area, shows the lowest diversity and evenness indices. Overall, mountainous areas have higher soil evenness when compared to basins. In terms of natural factors, temperature, precipitation, and topography play a crucial role in the variation of soil diversity in mountainous areas, while parent material has a greater influence in the basin regions. The characteristics of soil diversity vary by region and are influenced by the interactive effects of various natural factors. However, the impact of human activities also requires consideration. The low evenness poses a greater challenge for soil restoration in the basin regions. Soil conservation efforts in arid regions are of paramount importance. The research findings can provide valuable insights for the development of sustainable agriculture, soil conservation, and for addressing climate change challenges in arid regions.

Effects of land use and land cover change under shared socioeconomic pathways on future climate in the Yellow River basin, China

The Yellow River basin of China has experienced significant land use and land cover change (LUCC) due to excessive exploitation of nature resources, ecological degradation, and rapid urbanization, which plays an important role in the regional climate. It is crucial to explore the climate patterns of the basin under different future development scenarios to mitigate climate issues and achieve “carbon peaking and carbon neutrality goals”. Here we utilized land use and land cover (LULC) data and projections of future climate under two shared socioeconomic path – representative concentration path (SSP245 and SSP585) scenarios. Using the Weather Research and Forecasting (WRF) model, we simulated four future spatial variation patterns of temperature and precipitation in the basin. Results indicated that under the SSP245 and SSP585 scenarios, the temperature is projected to increase by 0.18 °C and 0.46 °C, respectively, while precipitation is expected to rise by 32.21 mm and 134.24 mm, respectively. The impact of LUCC was found to be relatively minor and mainly concentrated in the middle reaches of the basin. It resulted in a slight increase in temperature in both scenarios and an increase in precipitation in SSP245, but a decrease in precipitation in SSP585. Changes in farmland and urban area exhibited a certain warming effect in both scenarios, with urban areas having a greater influence, leading to a temperature increase by 0.25 °C and 0.28 °C, respectively. Forest, grassland, and bare areas had a lesser impact on temperature and showed different trends under two scenarios. Regarding precipitation, forests and urban areas had a greater influence in both SSP245 and SSP585 scenarios. This study identified the significant role of LUCC under different development scenarios in shaping future temperature and precipitation changes, providing valuable insights for effectively addressing climate issues in the Yellow River basin. It also highlights the need for clear policy recommendations and identifies institutions or agencies responsible for implementing such recommendations.

Choice of molecular marker influences spatial patterns of phylogenetic diversity.

Spatial patterns of phylogenetic diversity (PD) are increasingly becoming relevant for conservation decisions. PD measures are based on phylogenies estimated from molecular data. This paper addresses the question of how different molecular markers impact PD spatial patterns. We first conducted a simple simulation to explore the effect of deep and shallow changes in topology (simulating variations in molecular markers), using ultrametric and non-ultrametric trees, and then used a dataset of Chilean flora with four sets of markers to assess potential differences in spatial patterns of PD ranks using different markers and types of trees. The simulation consistently showed that the difference in PD rank was lower for ultrametric trees than for phylograms. A similar trend was observed using the Chilean flora dataset, with among-markers variability in spatial patterns of the PD metrics lower for ultrametric than for non-ultrametric trees, depicted as top 2.5 and 5% hotspots. Frequency distribution of PD values differed among markers as well, with this variation less apparent for ultrametric trees. We conclude that the choice of markers impacts spatial patterns of PD, and these results vary more strongly for phylograms, suggesting that ultrametric trees are more robust to the choice of marker.

North Atlantic Response to Observed North Atlantic Oscillation Surface Heat Flux in Three Climate Models

Abstract We investigate how the ocean responds to 10-yr persistent surface heat flux forcing over the subpolar North Atlantic (SPNA) associated with the observed winter NAO in three CMIP6-class coupled models. The experiments reveal a broadly consistent ocean response to the imposed NAO forcing. Positive NAO forcing produces anomalously dense water masses in the SPNA, increasing the southward lower (denser) limb of the Atlantic meridional overturning circulation (AMOC) in density coordinates. The southward propagation of the anomalous dense water generates a zonal pressure gradient overlying the models’ North Atlantic Current that enhances the upper (lighter) limb of the density-space AMOC, increasing the heat and salt transport into the SPNA. However, the amplitude of the thermohaline process response differs substantially between the models. Intriguingly, the anomalous dense-water formation is not primarily driven directly by the imposed flux anomalies, but rather dominated by changes in isopycnal outcropping area and associated changes in surface water mass transformation (WMT) due to the background surface heat fluxes. The forcing initially alters the outcropping area in dense-water formation regions, but WMT due to the background surface heat fluxes through anomalous outcropping area decisively controls the total dense-water formation response and can explain the intermodel amplitude difference. Our study suggests that coupled models can simulate consistent mechanisms and spatial patterns of decadal SPNA variability when forced with the same anomalous buoyancy fluxes, but the amplitude of the response depends on the background states of the models.

Epigenomic tomography for probing spatially defined chromatin state in the brain

Spatially resolved epigenomic profiling is critical for understanding biology in the mammalian brain. Single-cell spatial epigenomic assays were developed recently for this purpose, but they remain costly and labor intensive for examining brain tissues across substantial dimensions and surveying a collection of brain samples. Here, we demonstrate an approach, epigenomic tomography, that maps spatial epigenomes of mouse brain at the scale of centimeters. We individually profiled neuronal and glial fractions of mouse neocortex slices with 0.5mm thickness. Tri-methylation of histone 3 at lysine 27 (H3K27me3) or acetylation of histone 3 at lysine 27 (H3K27ac) features across these slices were grouped into clusters based on their spatial variation patterns to form epigenomic brain maps. As a proof of principle, our approach reveals striking dynamics in the frontal cortex due to kainic-acid-induced seizure, linked with transmembrane ion transporters, exocytosis of synaptic vesicles, and secretion of neurotransmitters. Epigenomic tomography provides a powerful and cost-effective tool for characterizing brain disorders based on the spatial epigenome.

High diversity within and low but significant genetic differentiation among geographic and temporal populations of the global Streptococcus pneumoniae.

Streptococcus pneumoniae is the major cause of invasive pneumococcal disease. However, the global population structure remains largely unexplored. In this study, we investigated the spatial and temporal patterns of genetic variation of S. pneumoniae based on archived multilocus sequence typing data from PubMLST.org. Our analyses demonstrated both shared and unique distributions of sequence types (STs) and allele types among regional populations. Among the 17 915 global STs, 36 representing 15 263 isolates were broadly shared among all six continents, consistent with recent clonal dispersal and expansion of this pathogen. The analysis of molecular variance revealed that>96% genetic variations were found within individual regional populations. However, though low (<4%), statistically significant genetic differentiation among regional populations was observed. Comparisons between non-clone-corrected and clone-corrected datasets showed that localized clonal expansion contributed significantly to the observed genetic differentiations among regions. Temporal analyses of the isolates showed that implementation of pneumococcal conjugate vaccine impacted the distributions of STs, but the effect on population structure was relatively limited. Linkage disequilibrium analyses identified evidence for recombination in all continental populations; however, the inferred recombination was not random. We discussed the limitations and implications of our analyses to the global epidemiology and future vaccine developments for S. pneumoniae.

GEOSPATIAL TECHNIQUES FOR MAPPING AND ANALYSIS OF CLIMATIC VARIABLES IN GOMBE STATE, NORTH-EAST NIGERIA.

This study was carried out to apply satellite- based climatic data in geospatial environment to downscale climatic data into smaller political units and to generate and analyse thematic maps of climatic variables in Gombe State. Spatial climatic data on rainfall, minimum and maximum temperature were obtained from DivaGIS climatic data, while water vapour, solar radiation and wind speed were derived from the WorldClim climatic data. Monthly climatic records from 1981-2021 in twenty-four points evenly spread across Gombe State on rainfall, minimum/maximum temperature and relative humidity were obtained from NASA Power Project to generate the trends and seasonal patterns of temperature and rainfall. Daily climatic records of rainfall were also acquired from globalweather climatic data between 1979 and 2013 and processed to derive the values of rainfall indices using Nieuwolt formula. The monthly climatic records were processed using the kriging module of ArcGIS to transform the point climatic data to spatial data. The result revealed that rainfall in Gombe State occurs mainly between May and September with the peak in August, while the trends in rainfall was decreasing at the southern and northern parts of the state but increasing at the central region. Rainfall onset occurs in the 1st or 2nd week of May in the south, but late (1st week of June) in the north. Rainfall cessation at the central and northern parts of the state occurs in the 3rd or 4th weeks of September, while the southern part experiences late cessation between the 1st and 3rd week of October. The LRS ranges from 95-111 and 160-175 days in the northern and southern parts respectively. High relative humidity was recorded between May and October with the peak in August. Spatial patterns of solar radiation and wind speed were also generated and analyzed. Awareness of the use of satellite based climatic data and geospatial techniques for downscaling climatic data were recommended in order to ease the problems of lack or inadequate climatic data for spatial mapping. Causal factors for the spatial patterns of climatic variables in Gombe state was suggested for further studies.

Spatial disparities and sources analysis of co-benefits between air pollution and carbon reduction in China

Spatial misallocation of resource elements impedes the coordination in regional co-benefit in carbon emission and pollution reduction. Guided by scope economy principles, this study aims to quantifies co-benefits and explores spatial variation patterns to facilitate mutual synergy. The findings offer valuable insights for identifying optimal focus points and shaping targeted collaborative policies. Based on the multi-source provincial panel data spanning 2000 to 2021, this paper quantitatively assesses the co-benefit through the lens of marginal abatement cost. Then, the Dagum Gini coefficient was employed to investigate the disparities at the spatial level. Additionally, geographical detector is introduced to analyze the source of disparities at the factor level. Results indicate: (1) Under joint reduction, the marginal abatement cost reduces by 57.86% and 79.97% respectively, with an overall 68.77% increase in co-benefit fluctuation. (2) Provinces with low co-benefit significantly decreased after 2007, while provinces with high co-benefit concentrated in the northwest, southwest, and east. (3) Overall disparities decreased, east-central regions hold the lowest spatial disparities in co-benefit and east-west hold the most; intragroup differences and supervariable density primarily contributes to the disparity. (4) FDI significantly influence the co-benefit over the past two decades, with spatial disparities influenced by both endogenous and exogenous factors across development stages.

Diversity of an Odonata assemblage from a tropical dry forest in San Buenaventura, Jalisco, Mexico (Insecta, Odonata).

The patterns of richness, diversity, and abundance of an odonate assemblage from San Buenaventura, Jalisco are presented here. A total of 1087 specimens from seven families, 35 genera and 66 species were obtained through monthly samplings of five days each during a period of one year. Libellulidae was the most diverse family (28 species), followed by Coenagrionidae (21), Gomphidae (7), Aeshnidae (6), Calopterygidae (2), Lestidae (1) and Platystictidae (1). Argia was the most speciose genus. The highest species richness and Shannon diversity were found during August and September, whereas the highest abundance was observed in June and the highest Simpson diversity was recorded in September - all of which were associated with the rainy season. The highest values of phylogenetic diversity were found from June to October. The different diversity facets of this assemblage were positively correlated with precipitation and minimum temperature, whereas maximum temperature showed no influence. In addition, we found that this odonate diversity was higher than most Mexican localities with tropical dry forest (TDF) studied. We continue our efforts to describe the patterns of richness, diversity and abundance of some insect groups associated with the tropical dry forest ecosystem in Mexico, following a latitudinal gradient of the distribution of this ecosystem in the country. Our emphasis here was to evaluate the spatial and temporal patterns of richness and diversity of an Odonata assemblage from Jalisco, Mexico.

Intrinsic Mode-Based Network Approach to Examining Multiscale Characteristics of Sea Surface Temperature Variability

Variability of sea surface temperature (SST), characterized by various spatiotemporal scales, is a proxy of climate change. A network analysis combined with empirical mode decomposition is newly presented for examining scale-dependent spatial patterns of SST variability. Our approach is applied to SST anomaly variability in the East/Japan Sea (EJS), consisting of satellite-derived daily datasets of 0.25° × 0.25° resolution from 1981 to 2023. Through the spatial distribution of instantaneous energy in intrinsic modes and features of intrinsic-mode networks, scale-dependent spatiotemporal features are found. The season-specific spatial pattern of energy density is observed only for weekly to semiannual modes, while a persistent high-energy distribution in the tongue-shaped region from East Korea Bay (EKB) to the Sub-Polar Front (SPF) is observed only for annual-to-decadal modes. The seasonality is apparent in the time evolution of energy only for weekly-to-annual modes, with a peak in summer and an increasing trend since the 2010s. Hubs of intrinsic-mode networks are observed in the whole southern area (some northern part) of EJS during the summer (winter), only for monthly to semiannual modes. Regional communities are observed only for weekly to seasonal modes, while there is an inter-basin community with annual-to-biennial modes, incorporating two pathways of East Sea Intermediate Water (ESIW).

Spatial pattern of urban forest diversity and its potential drivers in a snow climate city, Northeast China

Urban forest diversity is the cornerstone of providing ecosystem services and improving urban environment. However, the spatial pattern of urban forest (UF) diversity and its driving mechanisms are still not well understood. In our study, using 330 UF sample plots and the corresponding plot-based standard deviation values of remote sensing parameters with different spatial resolutions, we developed prediction models for the spatial pattern of UF diversity, and the driving factors of UF diversity were further explored in our study. Our findings indicated that the 3-m spatial resolution of remote sensing was optimal for predicting UF diversity. Both excessively high and excessively low resolutions can increase misclassification errors. In addition, among the three prediction models, the boosted regression trees (BRT) model exhibited greater robustness than the support vector machine (SVM) and random forest (RF) models for predicting UF diversity. UF species were abundant, with 112 tree species in Changchun City. Spatially, the UF diversity showed a significant gradient decreasing trend from suburban areas (outer rings) to downtown areas. We also observed higher diversity in the older communities with high housing prices, supporting the “legacy effect and luxury effect”. Additionally, our research revealed that socioeconomic factors, landscape cover, and patterns collectively explained 41.25% of the UF diversity variation, wherein socioeconomic factors contributed the most variation (17.33%). We further found that UF diversity does not increase more with increasing house prices when the house prices are higher than ¥ 12,000/m2. The higher patch density reduced UF diversity, while a higher UF landscape shape index (LSI) promoted diversity. The LSI threshold, which significantly impacted UF diversity, was about 12. Our research showed that high-resolution remote sensing offers a rapid, cost-effective approach to acquire UF diversity patterns, which could enhance urban forest diversity and contribute to sustainable urban development.

Nitrate Pollution Characteristics and Its Quantitative Source Identification of Major River Systems in China

Accurate source identification/apportionment is essential for optimizing water NO3--N pollution control strategies. This study conducted a meta-analysis based on data from 167 rivers across China from 2000 to 2022 to analyze the spatial and temporal variation patterns of nitrate pollution in seven major river systems and to quantitatively identify the source composition of riverine nitrate. The average ρ（NO3--N） in the seven major river systems was （4.54±3.99） mg·L-1， with 9.6% of river ρ（NO3--N） exceeding 10 mg·L-1. The riverine ρ（NO3--N） in eastern China were higher than that in western China， and the highest concentration was observed in the Haihe River system. Additionally， tributaries experienced more serious NO3--N pollution than that in the main stream. The ρ（NO3--N） in most river systems in the dry season was higher than that in the wet season， except in the Yellow River system. There was significant nitrification in the Pearl River system， the middle and lower reaches of the Yellow River system， the middle reaches of the Liaohe River system， the Songhua River system， and the Haihe River system， whereas there was significant denitrification in the Yangtze River system， the Huaihe River system， and the lower reaches of the Pearl River system. Based on the dual stable isotopes-based MixSIAR model， the major NO3--N source was sewage/manure （ &gt; 50%） in the Yangtze River system， Haihe River system， Liaohe River system， and Southeast River system. Soil nitrogen was the main NO3--N source in the Songhua River system （56.4%）， and the contribution of fertilizer nitrogen， soil nitrogen， and sewage/manure to NO3--N pollution in the Pearl River system， Huai River system， and Yellow River system was 20%-40%. The contribution rate of sewage/manure to NO3--N in the tributaries was higher than that in the main stream， whereas the contribution rate of soil nitrogen to NO3--N in the main stream was higher than that in the tributaries. The contribution rate of soil nitrogen， fertilizer nitrogen， and atmospheric deposition nitrogen to nitrate nitrogen in the wet season was higher than that in the dry season， whereas the contribution rate of sewage/manure to NO3--N pollution in the dry season was higher than that in the wet season. Therefore， point source pollution such as domestic and production sewage discharge should be controlled in the Haihe River system， the Yangtze River system， the Liaohe River system， the tributaries and the downstream main stream areas of Yellow River system， and the downstream area of the Pearl River system， whereas non-point source pollution caused by the loss of fertilizer and soil nitrogen should be controlled in the Huaihe River system， the Songhua River system， the middle reaches of the main stream area of the Yellow River system， and the middle and upper reaches of the Pearl River system. The results can provide a scientific basis for the effective control of nitrate pollution in the river systems in China.

Diurnal patterns of spatial stream temperature variations reveal the need for integrating thermal heterogeneity in riverscape habitat restoration

Longer durations of warmer weather, altered precipitation, and modified streamflow patterns driven by climate change are expected to impair ecosystem resilience, exposing freshwater ecosystems and their biota to a severe threat worldwide. Understanding the spatio-temporal temperature variations and the processes governing thermal heterogeneity within the riverscape are essential to inform water management and climate adaptation strategies. We combined UAS-based imagery data of aquatic habitats with meteorological, hydraulic, river morphology and water quality data to investigate how key factors influence spatio-temporal stream heterogeneity on a diurnal basis within different thermal regions of a large recently restored Danube floodplain. Diurnal temperature ranges of aquatic habitats were larger than expected and ranged between 14.2 and 28.0 °C (mean = 20.7 °C), with peak median temperatures (26.1 °C) around 16:00 h. The observed temperature differences in timing and amplitude among thermal regions were unexpectedly high and created a mosaic pattern of temperature heterogeneity. For example, cooler groundwater-influenced thermal regions provided several cold water patches (CWP, below 19.0 °C) and potential cold water refuges (CWRs) around 12:00 h, at the time when other habitats were warmer than 21.0 °C, exceeding the ecological threshold (20.0 °C) for key aquatic species. Within the morphological complexity of the restored floodplain, we identified groundwater influence, shading and river morphology as the key processes driving thermal riverscape heterogeneity. Promoting stream thermal refuges will become increasingly relevant under climate change scenarios, and river restoration should consider both measures to physically prevent habitat from excessive warming and measures to improve connectivity that meet the temperature requirements of target species for conservation. This requires restoring mosaics of complex and dynamic temperature riverscapes.

Comparative phylogeography shows congruent co‐divergence in Neotropical seasonally dry forest and savanna tree species

AbstractAimThe response of South American seasonally dry tropical forest (SDTF) and savanna species to the Quaternary climate changes is still poorly understood. Here we use multi‐model inference approach to compare the phylogeography and demographic history of five trumpet tree species (also known as pau‐d'arco) and draw general biogeographical patterns: Handroanthus impetiginosus, H. ochraceus, H. serratifolius, Tabebuia aurea and T. roseoalba.LocationAmazonia, Atlantic Forest, Cerrado and Caatinga ecoregions, South America and Brazil.TaxaBignoniaceae, Tabebuia Alliance, trees.MethodsWe compiled genetic data, occurrence records and ecological niche modelling (ENM) from published articles. We used coalescent modelling to test patterns of co‐divergence, and analysed niche breadth and overlap and spatial patterns in genetic diversity using quantile regressions.ResultsSpecies showed concordant lineage divergence times in the Pleistocene. Lineage diversification and phylogeographical patterns are more related to species niche breadth than to biome (savanna or SDTF). Differences in genetic diversity among species matches the expected for the demographic scenarios obtained with coalescent simulations and ENMs: species with wider range expansion during the Last Glacial Maximum had higher diversity than species with range retraction or multiple refugia.Main ConclusionsDiversification burst in the Pliocene, and major diversification of lineages during the Pleistocene seems to be a general pattern in SDTF and savannas in the South America. The high number of haplotypes at the Central Brazil, corresponding to the Cerrado ecoregion, highlights the importance of Protected Areas for SDTFs and savannas, highly threatened biomes in Brazil.

Grass Evolutionary Lineages Can Be Identified Using Hyperspectral Leaf Reflectance

AbstractHyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C4), Chloridoideae (C4), and Pooideae (C3). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf‐level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite‐based spectrometers.

The geno-geo-climate nexus: contributions of geographic and ecological factors in shaping the genomic divergence of two closely related threatened rainforest species of Fontainea Heckel (Euphorbiaceae)

ContextProcesses that shape genomic and ecological divergence can reveal important evolutionary dynamics to inform the conservation of threatened species. Fontainea is a genus of rainforest shrubs and small trees including critically endangered and threatened species restricted to narrow, but complex geographic and ecological regions. Several species of Fontainea are subject to spatially explicit conditions and experience limited intra-specific gene flow, likely generating genetic differentiation and local adaptation.ObjectivesHere, we explored the genetic and ecological mechanisms underlying patterns of diversification in two, closely related threatened Fontainea species. Our aim was to compare spatial patterns of genetic variation between the vulnerable Fontainea australis (Southern Fontainea) and critically endangered F. oraria (Coastal Fontainea), endemic to the heterogeneous subtropical region of central, eastern Australia, where large-scale clearing has severely reduced rainforest habitat to a fraction (< 1%) of its pre-European settlement extent.MethodsWe used a set of 10,000 reduced-representation markers to infer genetic relationships and the drivers of spatial genetic variation across the two species. In addition, we employed a combination of univariate and multivariate genome-environment association analysis using a set of topo-climatic variables to explore potential patterns of local adaptation as a factor impacting genomic divergence.ResultsOur study revealed that Coastal Fontainea have a close genetic relationship with Southern Fontainea. We showed that isolation by distance has played a key role in their genetic variation, indicating that vicariance can explain the spatial genetic distribution of the two species. Genotype-environment analyses showed a strong association with temperature and topographic features, suggesting adaptation to localised thermal environments. We used a multivariate redundancy analysis to identify a range of putatively adapted loci associated with local environmental conditions.ConclusionsDivergent selection at the local-habitat scale as a result of dispersal limitations and environmental heterogeneity (including physical barriers) are likely contributors to adaptive divergence between the two Fontainea species. Our findings have presented evidence to indicate that Southern and Coastal Fontainea were comprised of distinct genetic groups and ecotypes, that together may form a single species continuum, with further phenotype research suggested to confirm the current species boundaries. Proactive conservation actions, including assisted migration to enhance the resilience of populations lacking stress-tolerant single nucleotide polymorphisms (SNPs) may be required to secure the long-term future of both taxa. This is especially vital for the critically endangered Coastal Fontainea given projections of habitat decline for the species under future climate scenarios.