Distribution Of Plant Species Research Articles

Identifying and Managing Areas under Threat in the Iberian Peninsula: An Invasion Risk Atlas for Non-Native Aquatic Plant Species as a Potential Tool.

Predicting the likelihood that non-native species will be introduced into new areas remains one of conservation's greatest challenges and, consequently, it is necessary to adopt adequate management measures to mitigate the effects of future biological invasions. At present, not much information is available on the areas in which non-native aquatic plant species could establish themselves in the Iberian Peninsula. Species distribution models were used to predict the potential invasion risk of (1) non-native aquatic plant species already established in the peninsula (32 species) and (2) those with the potential to invade the peninsula (40 species). The results revealed that the Iberian Peninsula contains a number of areas capable of hosting non-native aquatic plant species. Areas under anthropogenic pressure are at the greatest risk of invasion, and the variable most related to invasion risk is temperature. The results of this work were used to create the Invasion Risk Atlas for Alien Aquatic Plants in the Iberian Peninsula, a novel online resource that provides information about the potential distribution of non-native aquatic plant species. The atlas and this article are intended to serve as reference tools for the development of public policies, management regimes, and control strategies aimed at the prevention, mitigation, and eradication of non-native aquatic plant species.

Investigation of the Rare Fraction of Flora in the Projected Landscape Reserve "Balka Shchurivska"

The study investigates the current distribution of rare plant species within the ravine system "Balka Shchurivska" and provides justification for granting the site protected area status. The research examines the presence of rare plant species listed in the Red Book of Ukraine, the Red List of Dnipropetrovsk Oblast, and international conservation lists. Field surveys conducted from 2020-2023 revealed the presence of 26 regionally rare plant species, 13 species listed in the Red Book of Ukraine, 3 species from Resolution 6 of the Bern Convention and annexes of the Habitats Directive represented in Ukraine's flora, and 1 species from the IUCN Red List. Notable rare species found include Pulsatilla pratensis, Adonis vernalis, and Stipa capillata. The study highlights the importance of the area for biodiversity conservation and recommends its designation as a landscape reserve of national importance.

Exploring the capability of high-resolution satellite data in delineating the potential distribution of common invasive alien plant species in the Tshivhase Tea Estate

Invasive alien plants (IAPs) continue to exert significant impacts on agriculture in many countries, resulting in food insecurity. IAPs reduce agricultural production through competition and parasitism with planted crops. More recently, the IAPs continue to extend their plasticity to tea plantations, especially in tropical and subtropical areas. This study thus aimed at exploring the potential of SPOT 7 and Sentinel 2 satellite data in mapping the occurrence and co-occurrence of three common IAPs Solanum mauritianum, Lantana camara, and Chromolaena odorata in the Tshivhase Tea Estate in Limpopo Province, South Africa. The stepwise logistic regression models were generated for Solanum mauritianum and Lantana camara occurrence as well as the observed and conditional co-occurrence probability of S. mauritianum (P1), L. camara (P2) and C. odorata (P3). From the remote sensing indices, the Brightness Index (BI) was significant in most SPOT 7 stepwise logistic regression models at p<0.05 whereas the blue, red, and near infrared (NIR) bands and standard deviation (STDv) variables were significant at p<0.05 in most of the Sentinel 2 models. The SPOT 7 model performed Sentinel-2 models, thus resulting in the area under the curve (AUC) of 0.96 for the conditional co-occurrence of S. mauritianum (P1) and L. camara (P2). The Sentinel 2 model yielded an AUC of 0.83. The SPOT 7 model performed superior in mapping the conditional co-occurrence of S. mauritianum and L. camara than the Sentinel 2 model. These results suggest that high spatial resolution satellite images like SPOT 7 can delineate the potential distribution of IAPs in the tea plantation and thus assisting in management strategies geared towards IAP’s elimination and control.

Trends and Patterns of Daily Maximum, Minimum and Mean Temperature in Brazil from 2000 to 2020

According to data obtained from meteorological towers, Brazil has significantly increased temperature in the past 20 years, particularly in the North and Midwest regions. Vapor pressure deficit and evapotranspiration were also analyzed, showing an increase across the entire country, confirming that the air is becoming drier. This warming trend is part of the global climate change phenomenon caused by the rise of greenhouse gases in the atmosphere, fires, poor soil management practices, deforestation, and logging. The increase in temperature and dryness has profoundly impacted Brazil’s climate and ecosystems, leading to intensified extreme weather events and changes in the distribution of both animal and plant species. This study highlights the importance of utilizing meteorological tower data to monitor and understand the effects of climate change in Brazil. It emphasizes the need for immediate action to address its causes and mitigate its negative impacts.

Diversity and distribution of invasive alien plant species along elevation gradient in Makawanpur district, central Nepal

Diversity and distribution of invasive alien plant species along elevation gradient in Makawanpur district, central Nepal

Trait driven or neutral: understanding the change in functional trait diversity during early plant succession using Price partitions

Habitat filtering, species interactions and neutral colonization as well as extinction dynamics govern the sequence of community assembly and functional diversity (FD) during primary plant succession. To study the factors that influence changes in FD we here use data on plant seed size, seed numbers and specific leaf area from 107 study plots along a 7 year sequence of primary succession (2005–2011) in a 6 ha German artificial catchment. We show that the temporal variability in functional diversity can be partitioned into the effects of trait expression, species richness and plant cover. We observed a dominant role of species richness and community composition on FD. Trade-offs in the influence of species richness and plant cover tended to decrease the change in FD. Average FD steadily increased during the first 4 years of succession (2005–2008). The degree of annual changes in FD were highly plot specific. Average change in FD was comparatively low during the first 4 years and later high. Soil characteristics and light conditions did not significantly influence the detectable change in functional diversity. We conclude that the high plot-specific spatial variability of the annual changes in FD transformed the initially catchment-wide homogeneous distribution of plant species into a mosaic of very different local plant communities. Our partitioning results also indicate that the successional sequences in FD are in accordance with a hidden Markov series.

Investigating the efficiency and capabilities of UAVs and Convolutional Neural Networks in the field of remote sensing as a land classification tool

The study aimed to determine the efficacy and capabilities of using high-resolution aerial imagery and a convolutional neural network (CNN) to identify plant species and monitor land cover and land change in the context of remote sensing. The full capabilities of a CNN were examined, including testing whether the platform could be used for land cover and the evaluation of land change over time. An unmanned aerial vehicle (UAV) was used to collect the aerial data of the study area. The CNN was encoded and operated in RStudio, while digitised data from the input imagery were used by the programme as training and validation data. The object in this respect was to learn about the relevant features of the landscape, and thereafter to classify the Opuntia invasive plant species. Accuracy assessments were carried out on the results to test the efficacy of the aerial imagery in terms of its accuracy and reliability. The classification achieved an overall accuracy of 93%, while the kappa coefficient score was 0.86. CNN was also able to predict the land coverage area of Opuntia to be within four percent (4%) of the ground truthing data. A change in land cover over time was detected by the programme after the manual clearing of the plant had been undertaken. This research has determined that the use of a CNN in remote sensing is a very powerful tool for supervised image classifications. It can be used for monitoring land cover in that it is able to accurately estimate the spatial distribution of plant species and to monitor the growth or decline in the species over time. As such, it is an efficient methodology and its use in remote sensing could be extended.

Resurgence of warmth-demanding tree species and a common pending thermophilization in subalpine and northern boreal Sweden-an ecological signal of post-Little Ice Age climate improvement

Largely consistent with general predictions and earlier empirical studies, it appears that post-Little Ice Age climate warming has started to affect large-scale biogeographic patterns in northern Sweden. Long-term monitoring in subalpine and adjacent regions reveals sparse spread of broadleaved thermophilic tree species. Saplings of Quercus robur, Ulmus glabra, Acer platanoides, Alnus glutinosa and Betula pendula have responded to recent climate warming by jump-dispersal in the order of 50-300 km northwards and 500-800 m upwards, relative to their natural range limits. Consistent with treeline rise by boreal tree species, the thermophilies have reinvaded regions where they grew during the warmest phase of the Holocene, 9500-8000 years ago, but were subsequently extirpated by the Neoglacial cooling. Confined to the past 20 years or so, the unique observations of recent termophilies comply with background climate data, i.e. warming of all seasons. These results may contribute to more realistic vegetation models by stressing that the distributions of certain plant species are able to track climate warming without substantial migrational lag. Hitherto, vegetation and climate evolution appear to be well within the frames of natural dynamics during the postglacial era, although mechanisms may differ.

A review of threatened plant species utilization, conservation status and distribution in the Vhembe biosphere reserve, Limpopo province, South Africa

A review of threatened plant species utilization, conservation status and distribution in the Vhembe biosphere reserve, Limpopo province, South Africa

Snow cover persistence as a useful predictor of alpine plant distributions

AbstractAimSnow cover persistence (SCP) has significant effects on plants in high‐elevation ecosystems. It determines the length of the growing season, provides insulation against low temperatures and influences water availability, thereby shaping the vegetation mosaic. Despite its importance, SCP is rarely used in plant species distribution modelling. In this study, we examine whether incorporating SCP in plant species distribution models (SDMs) improves their predictive power. We investigate the link between species' ecology and SDM improvements by the addition of various SCP predictors.LocationWestern Swiss Alps.Taxon206 alpine flowering plants (angiosperms).MethodsWe produced three maps of landsat satellite‐based SCP indices over an entire mountain region, one of them using an online open access platform allowing quick and easy replication and used them as a predictor in plant SDMs alongside commonly used predictors. We tested whether this improved the predictive performance of plant SDMs.ResultsAll three SCP indices improved the overall SDM predictive accuracy, but the overall improvement was potentially limited by their correlation with other climatic predictors. Alpine plant species known for their dependence on snow benefited more from the additional snow information.Main ConclusionsSCP should be used for predicting at least the distribution of alpine, snow‐related plant species. Given that adding snow cover improves SDMs and that snow duration decreases as climate warms, future predictions of alpine plant distributions should account for both snow predictor and associated snow change scenarios.

Contrasting patterns of host selection and performance of insect seed predators on three sympatric host plant species

Abstract Characterising host selection mechanisms and the performance of seed‐predating insects on different hosts has broad implications for ecology and applied entomology. We assessed host selection and performance of endophagous seed‐predating insects on three species of Copaifera, testing two hypotheses: (i) large‐sized trees produce larger seeds and have higher levels of seed predation than their smaller congeneric relatives, and (ii) endophagous seed‐predating insects that develop in larger‐seeded trees exhibit higher body mass, lipid content, and muscle mass as they obtain higher amount of food resources. Unpredated seeds of the large‐sized Copaifera langsdorffii showed greater mass than their smaller congeneric relatives C. arenicola and C. oblongifolia. Two species of endophagous seed‐predating beetles (Rhinochenus brevicollis and Apion sp.) were found attacking seeds of the three Copaifera species. Rhinochenus brevicollis preyed on a higher percentage of seeds than Apion sp. The shrub C. oblongifolia had the highest percentage of seed predation, contradicting our original hypothesis that larger species have a higher percentage of seed predation. Rhinochenus brevicollis preyed on a higher percentage of seeds of larger‐sized species than Apion sp. The higher dispersal ability of R. brevicollis associated with different patterns of plant species distribution may be the mechanism driving differential seed predation among Copaifera species. Adult insects that emerged from C. langsdorffii seeds showed higher body mass and lipidic content. It is likely that insects developing in larger seeds obtain a higher amount of food resources during their development. Finally, we observed a positive relationship between body weight and lipidic content in adults of R. brevicollis. We suggest that studies shedding light onto insect performance in different host‐sized seeds are necessary to understand host selection by beetles.

The genus Rhaponticum in Kazakhstan: species diversity, distribution and prospects for practical use. Review

The article presents information on the species composition and distribution of plants of the genus Rhaponticum in Kazakhstan based on literary sources and analysis of herbarium materials. The data of more than 300 samples of the herbarium collections were analyzed, including those from the Institute of Botany and Phytointroduction of the Republic of Kazakhstan, the Institute of Botany of the Academy of Sciences of the Republic of Uzbekistan, Moscow State University, IRPH “Phytochemistry”, the Institute of Biology and Soil of the National Academy of Sciences of the Republic of Kyrgyzstan. More than 30 literary sources were analyzed as well. 6 species of the genus Rhapontiсum were identified within the territory of Kazakhstan, which can be classified according to the following ecological and high-altitude groups. The groups include species growing in the subalpine and alpine meadows of Altai such as Rhaponticum carthamoides (Wiild.) Iljin; plain species such as Rh. Serratuloides (Georgi) Bobr., Rh. Nitidum Fisch.; plants growing on rocky and gravelly slopes of the Western Tien Shan such as Rh. Karatavicum Regel et Shcmalh., Rh. aulieatense Iljin, Rh. namanganicum (Iljin) Dittrich. Medicinal species Rh. carthamoides, Rh. serratuloides and Rh. karatavicum were analyzed to research future prospects of their usage. The perspectives of plants having adaptogenic, psychostimulant, tonic, immunostimulating, anabolic, antibacterial, antioxidant, hemorheological, antiviral, cytotoxic, antiprotozoal properties have been noted and discussed to be used in the medicine, food industry, fodder production, landscape design, beekeeping areas.

Pervasive impacts of climate change on the woodiness and ecological generalism of dry forest plant assemblages

Abstract Climate emergency is a significant threat to biodiversity in the 21st century, but species will not be equally affected. In summing up the responses of different species at the local scale, we can assess changes in the species quantity and composition of biotic assemblages. We used more than 420K curated occurrence records of 3060 plant species to model current and future patterns of plant species distribution in one of the world's largest tropical dry forests—the Caatinga. While allowing different model extrapolation scenarios, we estimated potential changes in the species richness and composition of dryland plant assemblages in response to projected climate change, and assessed how the ecological generalism and woodiness of plant assemblages can be impacted by the climate crisis. More than 99% of plant assemblages were projected to lose species by 2060, with biotic homogenisation—the decrease in spatial beta diversity—forecasted in 40% of the Caatinga. The replacement of narrow‐range woody species by wide‐range non‐woody ones should impact at least 90% of Caatinga plant assemblages. The exacerbated species loss in the dryland plant assemblages was connected to the heterogenisation and homogenisation of biotic assemblages. Still, the magnitude of climate change impacts on ecological generalism and woodiness patterns of dryland plant assemblages differ according to the direction of the biotic change process. Synthesis. The future increase in aridity will change the patterns of woodiness and ecological generalism of tropical dry forest plant assemblages by decreasing vegetation diversity and complexity. The projected biotic changes in dryland plant assemblages indicate the erosion of ecosystem services linked to biomass productivity and carbon storage. We highlight the importance of long‐term conservation planning for maintaining tropical dry forests.

A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs

Wildland conservation efforts require accurate maps of plant species distribution across large spatial scales. High-resolution species mapping is difficult in diverse, dense plant communities, where extensive ground-based surveys are labor-intensive and risk damaging sensitive flora. High-resolution satellite imagery is available at scales needed for plant community conservation across large areas, but can be cost prohibitive and lack resolution to identify species. Deep learning analysis of drone-based imagery can aid in accurate classification of plant species in these communities across large regions. This study assessed whether drone-based imagery and deep learning modeling approaches could be used to map species in complex chaparral, coastal sage scrub, and oak woodland communities. We tested the effectiveness of random forest, support vector machine, and convolutional neural network (CNN) coupled with object-based image analysis (OBIA) for mapping in diverse shrublands. Our CNN + OBIA approach outperformed random forest and support vector machine methods to accurately identify tree and shrub species, vegetation gaps, and communities, even distinguishing two congeneric shrub species with similar morphological characteristics. Similar accuracies were attained when applied to neighboring sites. This work is key to the accurate species identification and large scale mapping needed for conservation research and monitoring in chaparral and other wildland plant communities. Uncertainty in model application is associated with less common species and intermixed canopies.

Contrasting range changes of terrestrial orchids under future climate change in China

Climate change has impacted the distribution and abundance of numerous plant and animal species during the last century. Orchidaceae is one of the largest yet most threatened families of flowering plants. However, how the geographical distribution of orchids will respond to climate change is largely unknown. Habenaria and Calanthe are among the largest terrestrial orchid genera in China and around the world. In this paper, we modeled the potential distribution of eight Habenaria species and ten Calanthe species in China under the near-current period (1970–2000) and the future period (2081–2100) to test the following two hypotheses: 1) narrow-ranged species are more vulnerable to climate change than wide-ranged species; 2) niche overlap between species is positively correlated with their phylogenetic relatedness. Our results showed that most Habenaria species will expand their ranges, although the climatic space at the southern edge will be lost for most Habenaria species. In contrast, most Calanthe species will shrink their ranges dramatically. Contrasting range changes between Habenaria and Calanthe species may be explained by their differences in climate-adaptive traits such as underground storage organs and evergreen/deciduous habits. Habenaria species are predicted to generally shift northwards and to higher elevations in the future, while Calanthe species are predicted to shift westwards and to higher elevations. The mean niche overlap among Calanthe species was higher than that of Habenaria species. No significant relationship between niche overlap and phylogenetic distance was detected for both Habenaria and Calanthe species. Species range changes in the future was also not correlated with their near current range sizes for both Habenaria and Calanthe. The results of this study suggest that the current conservation status of both Habenaria and Calanthe species should be adjusted. Our study highlights the importance of considering climate-adaptive traits in understanding the responses of orchid taxa to future climate change.

Ecological Assessment of the Native Flora of Matta Kharari Village, Swat, Khyber Pakhtunkhwa, Pakistan

Plant species composition, diversity and distribution is a hierarchical expression of vegetation determined by different environmental factors. A study was conducted to document the floristic composition and biological spectrum of plant species of the Matta-Kharari region, Swat. The region has not been ecologically explored yet. We used quantitative ecological techniques for sampling plant species. Sixty-seven quadrats of 1x1 m2 for herbs, 5x5 m2 for shrubs and 10x10 m2 size for trees were established. Plant species data were analyzed using multivariate statistical techniques. We evaluated 76 plant species belonging to 42 families. Asteraceae and Rosaceae were the leading families with 8 species each, followed by Ranunculaceae, Lamiaceae and Moraceae with 4 species each. Among life form classes i.e., geophytes were dominant (13 species, 17.10 %), followed by therophytes (12 species, 15.87 %), nanophanerophytes (11 species, 14.47 %), and chamaephytes (10 species, 13.58 %). Moreover, Mesophylls (36.84 %) were the dominant leaf spectra class followed by nanophyll (30.26 %), microphyll (27.63 %), and megaphyll (2.63 %). The two-way cluster analysis classified the vegetation into three clusters. The detrended correspondence analysis shows the distribution of plants in a mixed array because the locations explored are characterized by similar climatic factors and vegetation. We elucidate that the variation in climatic factors and topography brings variation in vegetation. Understanding these responses at the life form and leaf spectra level will provide a better understanding and knowledge that how plant species and their communities or associations respond to changes in climate in the future. The current study could be utilized as a baseline for large-scale studies in the future.

Effects of climate change on the distribution of plant species and plant functional strategies on the Canary Islands

AbstractAimOceanic islands possess unique floras with high proportions of endemic species. Island floras are expected to be severely affected by changing climatic conditions as species on islands have limited distribution ranges and small population sizes and face the constraints of insularity to track their climatic niches. We aimed to assess how ongoing climate change affects the range sizes of oceanic island plants, identifying species of particular conservation concern.LocationCanary Islands, Spain.MethodsWe combined species occurrence data from single‐island endemic, archipelago endemic and nonendemic native plant species of the Canary Islands with data on current and future climatic conditions. Bayesian Additive Regression Trees were used to assess the effect of climate change on species distributions; 71% (n = 502 species) of the native Canary Island species had models deemed good enough. To further assess how climate change affects plant functional strategies, we collected data on woodiness and succulence.ResultsSingle‐island endemic species were projected to lose a greater proportion of their climatically suitable area (x ̃ = −0.36) than archipelago endemics (x ̃ = −0.28) or nonendemic native species (x ̃ = −0.26), especially on Lanzarote and Fuerteventura, which are expected to experience less annual precipitation in the future. Moreover, herbaceous single‐island endemics were projected to gain less and lose more climatically suitable area than insular woody single‐island endemics. By contrast, we found that succulent single‐island endemics and nonendemic natives gain more and lose less climatically suitable area.Main ConclusionsWhile all native species are of conservation importance, we emphasise single‐island endemic species not characterised by functional strategies associated with water use efficiency. Our results are particularly critical for other oceanic island floras that are not constituted by such a vast diversity of insular woody species as the Canary Islands.

Plant versus local soil inorganic ionic composition: The relationship to biomass smoke

We examine the relationship between soil and plant inorganic chemical composition as a precursor to biomass smoke aerosol particle (PM2.5) properties in desert landscapes of the Southwestern United States. Past work underscored the importance of plant species and in particular the dependence of smoke PM2.5 water uptake on the water-soluble inorganics important in select plant species (e.g., halophytes) versus absent in other species (e.g., conifers). This study extends this work by looking at a range of soil types and salinity in examining native and invasive species in the Desert Southwest US region. Eighteen plant samples and surrounding soils were taken from four ecosystems in New Mexico, USA. Results here support the conclusion that plant species are the primary controller over the inorganic plant composition that is relevant to biomass smoke and controls its hygroscopicity. The role of soil type is secondary to plant inorganic composition but is found to be important on the ecosystem level in determining what plant species are viable in a given ecosystem. This ultimately affects the smoke properties, including PM2.5 hygroscopicity (water uptake), produced in landscape fires. Knowledge of ecosystem features including plant species distribution and soil salinity may be combined as a first-order predictor of PM2.5 hygroscopicity of the primary smoke emissions. This can be particularly useful when combined with knowledge of burn characteristics such as flame temperature, which also plays a key role in determining PM2.5 water uptake response.

Spatio-temporal variation of water salinity in mangroves revealed by continuous monitoring and its relationship to floristic diversity

Salinity is among the most critical factors limiting the growth and species distribution of coastal plants. Water salinity in estuarine ecosystems varies temporally and spatially, but the variation patterns across different time scales and salinity fluctuation have rarely been quantified. The effects of salinity on floristic diversity in mangroves are not fully understood due to the temporal and spatial heterogeneity of salinity. In this study, we monitored water salinity at an interval of 10-min over one year in three mangrove catchment areas representing the outer part, middle part, and inner part respectively of Dongzhai Bay, Hainan, China. The number of mangrove community types and dominant mangrove species of the three catchment areas were also investigated. We found that the diurnal variation and dry-season intra-month variation in water salinity were driven by tidal cycles. The seasonal variation in water salinity was mainly driven by rainfall with higher salinity occurring in the dry season and lower salinity occurring in the wet season. Spatially, water salinity was highest at the outer part, intermediate at the middle part, and lowest at the inner part of the bay. The intra-month and annual fluctuations of water salinity were highest at the middle part and lowest at the outer part of the bay. The number of mangrove community types and dominant species were lowest at the outer part, intermediate at the middle part, and highest at the inner part of the bay. These results suggest that the temporal variation of water salinity in mangroves is driven by different factors at different time scales and therefore it is necessary to measure water salinity at different time scales to get a complete picture of the saline environment that mangroves experience. Spatially, lower salinity levels benefit mangrove species richness within a bay landscape, however, further research is needed to distinguish the effects of salinity fluctuation and salinity level in affecting mangrove species richness.

Field validation as a tool for mitigating uncertainty in species distribution modeling for conservation planning

AbstractDespite the success of species distribution models (SDMs) in rare species conservation planning, inherent uncertainty in predictions can facilitate misuse or misapplication. Field validation is important as model predictions can vary greatly due to low species prevalence or spatial bias; traits frequently encountered in rare species data. This study outlines an effective SDM approach to map the potential distribution of a Florida endemic plant species, Macbridea alba, which is transferable to other rare species that: (1) utilize presence data to create species distribution maps, (2) conduct field validation to gather independent data, (3) and use independent data for model evaluation and improvement. Independent data was used to assess each model's ability to predict the occurrence of M. alba and identify habitat for conservation and reintroduction efforts. Our study documents that for low prevalence species, models can produce significant variation in optimized threshold and evaluation metrics, which impact outcomes when converting continuous predictions to binary predictions, a common practice in model application. Our species distribution models were useful in finding new populations of M. alba. Field validation is vital to improving model performance, threshold selection, and evaluating model design and should be carefully considered when suggesting model use and application.