Occurrence Maps Research Articles

QEMSCAN® analysis of clay-rich stratigraphy associated with early agricultural contexts at Kuk Swamp, Papua New Guinea

Quantitative Evaluation of Minerals by Scanning Electron Microscope (QEMSCAN®) provides a diagnostic analytical tool to investigate clay-rich stratigraphy in early agricultural contexts at Kuk Swamp, Papua New Guinea. Previous in situ microstratigraphic analyses at Kuk, comprising thin section description and X-radiography, have been unable to differentiate and characterise clay mineralogy in the archaeological stratigraphy. Although prior X-ray diffraction provided compositional information on clay mineralogy for bulk samples, QEMSCAN® elemental and mineral analyses enable in situ identification and mapping of clay mineral occurrences and relationships. The results of the QEMSCAN® analyses permit reconstruction of the origin and post-depositional transformation of geogenic and biogenic sediments in clay-rich feature fills associated with former plant exploitation and cultivation. QEMSCAN® mineral analyses identify a positive association between smectite-rich contexts and biogenic silica (diatoms and phytoliths) and a negative association between kaolinite-rich contexts and biogenic silica. QEMSCAN® elemental analyses provide valuable information on neoformed minerals, which can be difficult to characterise optically within stratigraphy. These results show the value of QEMSCAN®, especially in combination with thin section micromorphology, to understand the origin and post-depositional formation of clay minerals at archaeological sites in the wet tropics, as well as in other environmental contexts.

Extension of occurrence and geographic distribution map of the toad Rhinella dapsilis (Myers & Carvalho, 1945) (Amphibia: Bufonidae) in the mid-north region of Brazil

Extension of occurrence and geographic distribution map of the toad Rhinella dapsilis (Myers & Carvalho, 1945) (Amphibia: Bufonidae) in the mid-north region of Brazil

Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal

Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faidherbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidherbia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.

SMODERP2D—Sheet and Rill Runoff Routine Validation at Three Scale Levels

Water erosion is the main cause of soil degradation in agricultural areas. Rill erosion can contribute vastly to the overall erosion rate. It is therefore crucial to identify areas prone to rill erosion in order to protect soil quality. Research on rainfall-runoff and subsequent sediment transport processes is often based on observing these processes at several scales, followed by a mathematical description of the observations. This paper presents the use of a combination of data obtained by different approaches at multiple scales to validate the SMODERP2D episodic hydrological-erosion model. This model describes infiltration, surface retention, surface runoff, and rill flow processes. In the model, the surface runoff generation is based on a water balance equation and is described by two separate processes: (a) for sheet flow, the model uses the kinematic wave approximation, which has been parameterized for individual soil textural classes using laboratory rainfall simulations, and (b) for rill flow, the Manning formula is used. Rill flow occurs if the critical water level of sheet flow is exceeded. The concept of model validation presented here uses datasets at different scales to study the surface runoff and erosion processes on the Býkovice agricultural catchment. The first dataset consisted of runoff generated by simulated rainfall on plots with dimensions of 2 × 8 m. The second dataset consisted of the runoff response to natural rainfall events obtained from long-term monitoring of 50 m2 plots. These two datasets were used to validate and calibrate the sheet flow and infiltration parameters. The third dataset consisted of occurrence maps of rills formed during heavy rainfalls obtained using remote sensing methods on a field plot with an area of 36.6 ha. This last dataset was used to validate the threshold critical water level that is responsible in the model for rill flow initiation in the SMODERP2D model. The validation and the calibration of the surface runoff are performed well according to the Nash–Sutcliffe efficiency coefficient. The scale effect was evident in the 50 m2 plots where parameters lower than the mean best fit the measured data. At the field plot scale, pixels with measured rills covered 5% of the total area. The best model solution achieved a similar rill cover for a vegetated soil surface. The model tended to overestimate the occurrence of rills in the case of simulations with bare soil. Although rills occurred both in the model and in the monitored data in many model runs, a spatial mismatch was often observed. This mismatch was caused by flow routing algorithm displacement of the runoff path. The suitability of the validation and calibration process at various spatial scales has been demonstrated. In a future study, data will be obtained from various localities with various land uses and meteorological conditions to confirm the transferability of the procedure.

Ambientes de ocorrência do porífero Oncosclera jewelli no Parque Estadual Do Tainhas, Planalto Meridional do RS: uma análise geomorfométrica

O Parque Estadual do Tainhas abrange ambientes de ocorrência do porífero Oncosclera jewelli. Nosso objetivo é indicar as áreas de potencial ocorrência da espécie a partir de fatores relacionados à assinatura geomorfométrica dos pontos de ocorrência ao longo do Rio Tainhas no Parque e sua zona de amortecimento. Foram realizados cruzamentos e análises de dados a partir da construção e manipulação de um banco de dados geográficos, nos SIGs SPRING-5.4.3 e QGIS-2.18, contendo: a) MDEs oriundos das bases Topodata, Embrapa e Alos, obtidos por sensores remotos orbitais para toda a área de estudo, e MDE obtido por imagens aéreas geradas por Drone e as variáveis geomorfométricas produzidas: declividade, aspecto e índice de posição topográfica; b) mapa geológico do Rio Grande do Sul; c) mapa de uso e cobertura do solo, com base em imagens do satélite GeoEye. Os resultados demonstraram que a espécie ocorre exclusivamente sobre a unidade litológica da Formação Serra Geral. O relevo é marcado por terrenos de baixa declividade e orientação sul e leste e em relevo aplainado. Foi gerado o Mapa de Potencial Ocorrência da espécie, demonstrando que aproximadamente 4,5% do comprimento total dos trechos de cursos d’água na área de estudo reúnem as condicionantes geomorfométricas identificadas como de potencial ocorrência da espécie.

Mapping the biotic degradation hazard of wood in Europe – biophysical background, engineering applications, and climate change-induced prospects

AbstractConstruction using timber has seen a resurgence in light of global climate mitigation policies. Wood is a renewable resource, and engineered wood products are proving to be competitive against concrete and steel while having several advantages. However, while the renewable nature of wood in construction is a beneficial property for climate mitigation policies, the process of biodegradation introduces a challenge for service life planning. A review of hazard mapping is presented while developing contemporary hazard maps, occurrence maps and projected hazard maps for 2050 using representative concentration pathways (RCP) 2.6 and 8.5. The risk of timber decay is expected to increase in most of Europe as the temperatures rise, with a decrease expected in dryer regions. Termites are likely to experience a range expansion as more areas become suitable, while human activity and an increase in extreme weather events like floods are expected to facilitate dispersion. Marine borer species already present a risk in most European coastal regions; however, the effect of changes in water temperatures are likely to shift the boundaries for individual borer species. Overall, warmer climates are expected to increase the metabolic activity of all of these organisms leading to a general reduction in service life.

A systematic review and meta-analysis of the potential non-human animal reservoirs and arthropod vectors of the Mayaro virus.

Improving our understanding of Mayaro virus (MAYV) ecology is critical to guide surveillance and risk assessment. We conducted a PRISMA-adherent systematic review of the published and grey literature to identify potential arthropod vectors and non-human animal reservoirs of MAYV. We searched PubMed/MEDLINE, Embase, Web of Science, SciELO and grey-literature sources including PAHO databases and dissertation repositories. Studies were included if they assessed MAYV virological/immunological measured occurrence in field-caught, domestic, or sentinel animals or in field-caught arthropods. We conducted an animal seroprevalence meta-analysis using a random effects model. We compiled granular georeferenced maps of non-human MAYV occurrence and graded the quality of the studies using a customized framework. Overall, 57 studies were eligible out of 1523 screened, published between the years 1961 and 2020. Seventeen studies reported MAYV positivity in wild mammals, birds, or reptiles and five studies reported MAYV positivity in domestic animals. MAYV positivity was reported in 12 orders of wild-caught vertebrates, most frequently in the orders Charadriiformes and Primate. Sixteen studies detected MAYV in wild-caught mosquito genera including Haemagogus, Aedes, Culex, Psorophora, Coquillettidia, and Sabethes. Vertebrate animals or arthropods with MAYV were detected in Brazil, Panama, Peru, French Guiana, Colombia, Trinidad, Venezuela, Argentina, and Paraguay. Among non-human vertebrates, the Primate order had the highest pooled seroprevalence at 13.1% (95% CI: 4.3–25.1%). From the three most studied primate genera we found the highest seroprevalence was in Alouatta (32.2%, 95% CI: 0.0–79.2%), followed by Callithrix (17.8%, 95% CI: 8.6–28.5%), and Cebus/Sapajus (3.7%, 95% CI: 0.0–11.1%). We further found that MAYV occurs in a wide range of vectors beyond Haemagogus spp. The quality of evidence behind these findings was variable and prompts calls for standardization of reporting of arbovirus occurrence. These findings support further risk emergence prediction, guide field surveillance efforts, and prompt further in-vivo studies to better define the ecological drivers of MAYV maintenance and potential for emergence.

Sporadic E morphology based on COSMIC radio occultation data and its relationship with wind shear theory

The S4max data retrieved from the Constellation Observing System for the Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) measurements during 2007 to 2015 is adopted to investigate the global distribution and seasonal variation of the sporadic E (Es) layers in the present work. The long-term and short-term global Es occurrence maps are presented and the spatial and temporal distributions of Es occurrence rates (ORs) are further confirmed and studied. The International Geomagnetic Reference Field model (IGRF12) is used to calculate the horizontal intensity and inclination of the Earth’s magnetic field. The analysis shows that the Earth’s magnetic field is one of the fundamental reasons for the global distribution of the Es layers. In addition, the Horizontal Wind Field model HWM14 and the IGRF12 model were employed to calculate the vertical ion convergence (VIC) to examine the role of neutral wind shear in the global distribution of the Es ORs. The results reveal that the middle latitude distribution of simulated vertical concentration of Fe+ is similar to that of Es ORs, which indicates that the VIC induced by the neutral wind shear is an important factor in determining the geographical distribution, summer maximum (or winter minimum) and diurnal characteristics of Es ORs in middle latitudes. The new findings mainly include the following two aspects: (1) in summer over mid-latitudes, VIC peaks in the morning and afternoon to evening, which explains the semidiurnal behavior of Es ORs; (2) VIC reaches its minimum value in low-altitude (100 km) areas, which is the reason for the significant decrease in Es ORs in low-altitude areas. The disagreements between the VIC and Es ORs indicate that other processes, such as the meteor influx rate, the ionospheric electric fields and atmospheric tides, should also be considered as they may have an important impact on the variation of Es layers.Graphical

Maps of relative floristic ignorance and virtual floristic lists: An R package to incorporate uncertainty in mapping and analysing biodiversity data

The vast amount of occurrence records currently available offers increasing opportunities for biodiversity data analyses. This amount of data poses new challenges for the reliability and correct interpretation of the results. Indeed, to safely deal with occurrence records, their uncertainty and associated biases should be taken into account. We developed an R package to explicitly include spatial and temporal uncertainties during the mapping and listing of plant occurrence records for a given study area. Our workflow returns two objects: (a) a ‘Map of Relative Floristic Ignorance’ (MRFI), which represents the spatial distribution of the lack of floristic knowledge; (b) a ‘Virtual Floristic List’ (VFL), i.e. a list of taxa potentially occurring in the area with an associated probability of occurrence. The method implemented in the package can manage a large amount of occurrence data and represents relative floristic ignorance across a study area with a sustainable computational effort. Several parameters can be set by the user, conferring high flexibility to the method. Uncertainty is not avoided, but incorporated into biodiversity analyses through appropriate methodological approaches and innovative spatial representations. Our study introduces a workflow that pushes forward the analytical capacities to deal with uncertainty in biological occurrence records, allowing to produce more accurate outputs.

Machine learning techniques for acid sulfate soil mapping in southeastern Finland

Acid sulfate soils are one of the most environmentally harmful soils existing in nature. This is because they produce sulfuric acid and release metals, which may cause several ecological damages. In Finland, the occurrence of this type of soil in the coastal areas constitutes one of the major environmental problems of the country. To address this problem, it is essential to precisely locate acid sulfate soils. Thus, the creation of occurrence maps for these soils is required. Nowadays, different machine learning methods can be used following the digital soil mapping approach. The main goal of this study is the evaluation of different supervised machine learning techniques for acid sulfate soil mapping. The methods analyzed are Random Forest, Gradient Boosting and Support Vector Machine. We show that Gradient Boosting and Random Forest are suitable methods for the classification of acid sulfate soils, the resulting probability maps have high precision. However, the accuracy of the probability map created with Support Vector Machine is lower because this method overestimates the non-AS soils occurrences. We also compare these modeled probability maps with the conventionally produced occurrence map. In general, the modeled maps are more objective and accurate than the conventional maps. Moreover, the mapping process using machine learning techniques is faster and less expensive.

Bibliometric analysis of research in the field of organizational communication in the web of science database

For organizations, communication is one of the most critical factors affecting their continuity, goals, and success levels. Organizational communication directs the relationship between internal and external stakeholders of the organization by taking a role in all organizational action and managerial processes. In this context, it also affects organizational outcomes. Effectively and efficiently channelling intra-organizational communication for organizational success is also effective in employees' understanding of their duties and responsibilities within the organization and activating their knowledge skills and abilities in line with the organisation's goals. At this point, it can be said that organizational communication adds mobility to businesses through self-expression. From this perspective, it understands the communication subject's development processes that play a crucial role for organizations in the literature and revealing its relationship with other variables will bring a systematic and holistic perspective to the relevant literature. With the bibliometric analysis method made for this purpose, it is aimed to create a perspective on how organizational communication offers mobility to businesses, the development, quality and quantity of the process. In this context; Distribution of studies on organizational communication by years, co-authorship of authors, co-authorship of organizations, co-authorship of countries, citation of authors, bibliographic coupling of documents, co-citation of authorship, co-citation of sources, co- The maps of occurrence of keywords were created, and the levels of contribution to the literature and the areas where the subject interacts were conveyed.

Hohenbergia densa (Bromeliaceae), a new species from Rio Grande do Norte, Brazil

We describe a new species from Rio Grande do Norte (Brazil), Hohenbergia densa, that inhabits the dry vegetation of the state. This new species can be recognized by its short and robust habit, differing from other Caatinga-endemic species of the region, that commonly present a broader rosette with a long inflorescence axis. Additionally, we provide illustrations, habit, phenology, a map of occurrence, and a table of morphological comparisons with similar taxa.

Delineation of shallow groundwater potential zones using integrated hydrogeophysical and topographic analyses, western Saudi Arabia

Groundwater potentiality mapping is currently receiving more attention, particularly in arid regions, where natural freshwater resources are limited and scarce and instead costly unconventional methods such as seawater desalination are widely used to satisfy water demands. Water demand in Makkah city is considered a vital task, which receives millions of visitors every year for religious purposes. The present study evaluates the potential zones of groundwater in the southwest Makkah city applying hydrogeophysical modeling approach that integrates geoelectrical resistivity in form of vertical electrical sounding (VES), ground-penetrating radar (GPR), and topographic analyses. VES results indicate five main geoelectric layers: (1) dry wadi-fill deposits with a true resistivity varying from 73 to 650 Ohm.m and average thickness of 3.14 m, (2) partially saturated sands with a resistivity range between 19 and 43 Ohm.m and average thickness of 13 m, (3) water-bearing layer of saturated sands with a resistivity range between 55 and 110 Ohm.m, and average thickness of 52 m, (4) highly fractured diorite rocks with resistivity values ranging from 750 to 1450 Ohm.m and average thickness of 1.5 m, and (5) fresh diorite basement rocks with a resistivity of more than 4000 Ohm.m. Results of GPR profiles support the gained results of the VES technique, and together they are confirmed by the wells data. The integrated results suggest a structural control on the groundwater accumulation and call for a detailed mapping of alluvium-covered faults to provide a high-resolution mapping of groundwater occurrences and ensure environmental sustainability in southwest Makkah city.

Hydrothermal Alteration of the Ocean Crust and Patterns in Mineralization With Depth as Measured by Micro-Imaging Infrared Spectroscopy.

Processes for formation, cooling, and altering Earth's ocean crust are not yet completely understood due to challenges in access and sampling. Here, we use contiguous micro‐imaging infrared spectroscopy to develop complete‐core maps of mineral occurrence and investigate spatial patterns in the hydrothermal alteration of 1.2 km of oceanic crust recovered from Oman Drilling Project Holes GT1A, GT2A, and GT3A drilled in the Samail Ophiolite, Oman. The imaging spectrometer shortwave infrared sensor measured reflectance of light at wavelengths 1.0–2.6 μm at 250–260 μm/pixel, resulting in >1 billion independent measurements. We map distributions of nine key primary and secondary minerals/mineral groups—clinopyroxene, amphibole, calcite, chlorite, epidote, gypsum, kaolinite/montmorillonite, prehnite, and zeolite—and find differences in their spatial occurrences and pervasiveness. Accuracy of spectral mapping of occurrence is 68%–100%, established using X‐ray diffraction measurements from the core description. The sheeted dikes and gabbros of upper oceanic crust Hole GT3A show more pervasive alteration and alteration dominated by chlorite, amphibole, and epidote. The foliated/layered gabbros of GT2A from intermediate crustal depths have similarly widespread chlorite but more zeolite and little amphibole and epidote. The layered gabbros of the lower oceanic crust (GT1A) have remnant pyroxene and 2X less chlorite, but alteration is extensive within and surrounding major fault zones with widespread occurrences of amphibole. The results indicate greater distribution of higher temperature alteration minerals in the upper oceanic crust relative to deeper gabbros and highlight the importance of fault zones in hydrothermal convection in the lower ocean crust.

Predicting potential occurrence of pine wilt disease based on environmental factors in South Korea using machine learning algorithms

Pine wilt disease (PWD) is one of the most destructive diseases affecting pine trees, and South Korea is one of the most severely damaged countries in the world. Based on the information on PWD occurrence and their environmental characteristics (i.e., geographical, meteorological, and land-use) in South Korea, we evaluated the conditions most conducive for PWD occurrence and developed projection models using machine learning algorithms; random forest (RF) and maximum entropy (Maxent). Our results showed that PWD mainly occurred in areas like highly urbanized area; low elevations, at close proximity to roads. Also, both RF and Maxent models presented high prediction performance for PWD occurrence. Geographical factors (e.g., elevation and distance to roads) were major determinants of PWD occurrence and largely contributed to explaining variability and partial dependence plots of each model. We developed an ensemble model composed of the RF and Maxent models to predict a potential risk map for PWD occurrence on a national scale. In South Korea, most territory was included potential risk of PWD occurrence, and it was predicted to be expanded in the future according to the climate change. The study results showed a high utility for use in surveillance and monitoring of PWD occurrence by inferring the spread pathway or spread direction of PWD based on the potential occurrence map.

Monitoring spatial-temporal dynamics of small lakes based on SAR Sentinel-1 observations: a case study over Nui Coc Lake (Vietnam)

For the first time, this study estimates the variation of surface water extent of Nui Coc Lake located in Thai Nguyen province in North Vietnam at high spatial (20 m) and temporal resolution (bi-weekly). The classification methodology was developed based on the use of the Otsu threshold algorithm on the histogram of the backscatter coefficient of the SAR Sentinel-1 signal. Totally, more than 150 SAR Sentinel-1 images have been processed for the 2016-2020 period. Except for extreme drought and flood conditions, the average minimum and maximum of the lake’s surface water extent are 17 km2 (in May) and 24 km2 (in September/October), respectively, and Nui Coc Lake’s surface water was stable during the last five years. Classification results are in good agreement with the corresponding surface water extent maps derived from free-cloud Sentinel-2 images, with the occurrence map derived from the Landsat-derived Global Surface Water (GSW) product, and with in situ precipitation data. Compared to Sentinel-2, the lake’s surface water extent detected from Sentinel-1 is 4-4.5% less. The water occurrence is similar between our results and that derived from the GSW product, but Sentinel-1 data provide more details as its spatial resolution is higher than Landsat. This study clearly shows the great potential of SAR Sentinel-1 data for monitoring small lake’s water surface at low costs, especially over tropical regions.

Utilizing User-Generated Content and GIS for Flood Susceptibility Modeling in Mountainous Areas: A Case Study of Jian City in China

Floods are threats seriously affecting people’s lives and property globally. Risk analysis such as flood susceptibility assessment is one of the critical approaches to mitigate flood impacts. However, the inadequate field survey and lack of data might hinder the mapping of flood susceptibility. The emergence of user-generated content (UGC) in the era of big data provides new opportunities for flood risk management. This research proposed a flood susceptibility assessment model using UGC as a potential data source and conducted empirical research in Ji’an County in China to make up for the lack of ground survey data in mountainous-hilly areas. This article used python crawlers to obtain the geographic location of the floods in Ji’an City from 2016 to 2019 from social media, and the state-of-the-art MaxEnt algorithm was adopted to obtain the flood occurrence map. The map was verified by the flood data crawled from reliable official media, which achieved an average AUC of 0.857% and an overall accuracy of 93.1%. Several novel indicators were used to evaluate the importance of conditioning factors from different perspectives. Land use, slope, and distance from the river were found to contribute most to the occurrence of floods. Our findings have shown that the proposed historical UG C-based model is practical and has good flood-risk-mapping performance. The importance of the conditioning factors to the occurrence of floods can also be ranked. The reports from stakeholders are a great supplement to the insufficient field survey data and tend to be valuable resources for flood disaster preparation and mitigation in the future. Finally, the limitations and future development directions of UGC as a data source for flood risk assessment are discussed.

Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China

Although low-intensity forest fires are a necessary part of healthy echo system, high-intensity forest fires continue to affect the diversity of forest ecosystems and species. Therefore, it is necessary to study the driving factors of forest fires and explore their possible locations and probabilities in complex forest terrain. In this article, we determined the relative influences of different types of factors on the occurrence of forest fires in Heilongjiang forest areas and compared the performance of artificial neural networks and logistic regression for wildfire prediction. By analyzing Heilongjiang forest fire data from 2002 to 2015 and constructing a model, we found that climate factors, topographical factors, and vegetation type factors play a crucial role in Heilongjiang’s wildfires. During the fire prevention period, temperature and wind speed have a more significant influence than other factors. According to the variable screening that we conducted, the model built by the variables that we used can predict 84% of forest fires in Heilongjiang Province. For recent wildfires (2019–2020) in most areas, we can use artificial neural networks for relatively accurate verification (85.2%). Therefore, artificial neural networks are very suitable for the prediction of forest fires in Heilongjiang Province. Through the prediction results, we also created a probability distribution map of fire occurrence in the study area. On this basis, we also analyzed the changes in the probability of natural fires under the weather changing trend, which can effectively aid in fire prevention and extinguishment.

Climate and geological change as drivers of Mauritiinae palm biogeography

ABSTRACTAimForest composition and distribution are determined by a myriad of factors, including climate. As models of tropical rain forest, palms are often used as indicator taxa, particularly the Mauritiinae. We question, what characterizes the Mauritiinae pollen in the global fossil record? And when did the Mauritiinae become endemic to South America?LocationGlobal tropics.TaxonMauritiinae palms (Arecaceae: Lepidocaryeae).MethodsPollen trait data from extinct and extant Mauritiinae pollen were generated from light‐, scanning‐, and transmission electron microscopy. Statistical morphometric analysis was used to define species and their relationships to other Mauritiinae. We also compiled a comprehensive pollen database for extinct and extant Mauritiinae and mapped their global geographical distribution from Late Cretaceous to present, using GBIF and fossil data.ResultsOur morphometric analysis identified 18 species (11 extinct and seven extant), all exhibiting exine indentations, a synapomorphy of the subtribe. The fossil taxa and early divergent extant Lepidocaryum are all monosulcate, whereas the extant Mauritia and Mauritiella species are all monoulcerate. Paleobiogeographical maps of fossil Mauritiinae pollen occurrences suggest the taxon originated in equatorial Africa during the Cretaceous, and expanded their range to South America, and to India in the Paleocene. Range retraction started in the early Eocene with extirpation from India, and reduction in diversity in Africa culminating at the Eocene–Oligocene Transition (EOT). In contrast, in South America, the distribution is maintained, and since the Neogene Mauritiinae palms are mostly restricted to swampy, lowland habitats.Main conclusionsMorphometric analysis shows that since their origin Mauritiinae pollen are relatively species poor, and Mauritiidites resembles Lepidocaryum. We also conclude that the biogeographical history of the Mauriitinae and, by extension, tropical forests was strongly affected by global climatic cooling events. In particular, the climate change at the EOT was a fundamental determinant of current tropical forest distribution.

Prioritizing areas for conservation outside the existing protected area network in Bhutan: the use of multi-species, multi-scale habitat suitability models

ContextUnderstanding the environmental and anthropogenic factors influencing habitat selection of multiple species is a foundation for quantifying human impacts on biodiversity and developing effective conservation measures.ObjectivesTo determine the effect of multiple scales of environmental/topographic and anthropogenic variables and landscape patterns on habitat suitability of terrestrial mammals in Bhutan, assess the effectiveness of the current protected area network, identify areas of high species richness outside of the existing protected area, and evaluate the potential effectiveness of indicator and umbrella species for conservation planning.MethodsWe modelled multi-scale habitat selection of sixteen species of terrestrial mammals across Bhutan using data from a nation-wide camera trap survey. We used the predicted species distribution maps to assess the multi-species conservation effectiveness of the existing protected area network. We performed simulations to identify high priority areas for multiple species based on their habitat suitability, proximity to existing protected areas and overall connectivity within the predicted distribution of species. We used correlation analysis among predicted occurrence maps and multivariate cluster analysis to identify potential indicator species. We evaluated the potential utility of each species as umbrella species by assessing how well optimal protected areas for that species would protect suitable habitat for all 16 species simultaneously.ResultsProtected areas and forest cover were strongly associated with habitat use of most modelled species. Additionally, topographical features, like terrain roughness and slope position, contributed to habitat selection of multiple species, but often in different ways. Environmental and topographical variables were mostly selected at medium to broad scales. Anthropogenic variables (agriculture and built-up areas) were negatively associated with habitat suitability of most species at both fine and broad scales. Conservation effectiveness assessment of existing protected areas found protected areas in south-central Bhutan have high effectiveness in terms of both mean and total richness protected. Similarly, biological corridors in the south-central region offered high mean richness protection. Our simulation of optimal areas for additional protection found areas abutting protected areas in southern Bhutan offered high relative species richness protection. Our umbrella species analysis found muntjac, wild pig, serow, sambar and Asian golden cat are the most effective umbrella species for broader biodiversity protection. Our indicator species analysis found tiger, gaur, dhole, clouded leopard, Asian black bear and common leopard as effective indicator species.ConclusionsThis study highlights the need to protect optimally located species-rich areas outside the current protected areas. This kind of multi-species habitat assessment provides important information to optimize future conservation and development plans at national and regional scales.