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Several stages of Hyptis invasion in the landscape of the study area: a early stage of Hyptis invasion, b riverine landscape heavily invaded by Hyptis, c healthy patches of Hyptis (below red line) in the area near to habitation and agriculture fields, d larger patch of landscape having invasion of Hyptis competing with Lantana, e a close view of Hyptis plant, f late summer period with sign of dried population of Hyptis
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Biological invasion is probably one of the most serious threats to biodiversity after climate change. Landscape distinguished by the heterogeneity of structure, forms, human interferences, and environmental settings plays an important role in the establishment and spread of invasive species. We investigated the effect of the spatial heterogeneity f...
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The assessment of seed banks could provide useful hints towards ensuring restoration planning and invasive species management. In this study, the impacts of two invaders, Hyptis suaveolens and Urena lobata on the soil seed banks were investigated. We also assessed the seed characteristics of the invaders at the invaded sites. This was achieved usin...
Citations
... In interior forest openings of the Vindhyan highlands, heavy infestations of H. suaveolens monoculture thickets have also been reported (Sharma 2007b;Sharma et al. 2009;Sharma and Raghubanshi 2009;Sharma et al. 2017). Kumar et al. (2019) advocated that habitat size, land use, and management practices affect the H. suaveolens invasive population and reported a severe infestation of H. suaveolens in open areas of water stream networks of Indian Western Himalayan regions. Studies have indicated that H. suaveolens forms dense monospecific thickets, potentially reducing light penetration to the forest floor, thus masking the growth of other herbaceous species (Raizada 2006;Sharma et al. 2009Sharma et al. , 2017Afreen et al. 2018). ...
... Spatial heterogeneity, management practices, and environmental factors such as land disturbances (Minor et al. 2009;Vilà et al. 2011;Kumar et al. 2019), species composition (Stohlgren et al. 1998), soil properties (Fukano et al. Fig. 7 Relationships between and among stand-level, seed-level, and plant-level traits of Hyptis suaveolens under sun (high-light) and shade (low-light) conditions. ...
Background
Hyptis suaveolens (L.) Poit., native to the American tropics, is a pantropical annual plant and a major invasive species throughout India. It was anticipated that the availability of sunlight, coupled with its superior reproductive potential, persistent propagule bank, and dispersal ability, could lead to an increase in the growth and spread of this invader, thus potentially impeding herbaceous growth and diversity in non-native areas. Clarifying its ecological fitness and competitive performance will be useful to manage the spread of H. suaveolens in natural ecosystems that are facing a wide range of anthropogenic pressures.
Methods
The present study is a three-tier experiment. In the first tier, a field study was conducted to assess the patterns of H. suaveolens abundance and herbaceous species diversity in response to light availability (sun, 842–1072 µmol m –2 s ⁻¹ and shade 253–341 µmol m – ² s ⁻¹ ) in the tropical dry deciduous ecosystems in the Vindhyan highlands, India. Furthermore, the impact of H. suaveolens abundance on the resident native and non-native species abundance and diversity was also studied. In the second tier, a randomized common garden experiment was conducted to understand the trait fitness of H. suaveolens in sun (940 µmol m –2 s ⁻¹ ) and shade (300 µmol m –2 s ⁻¹ ) conditions. In the third tier, a plant growth chamber experiment with high-light (940 µmol m –2 s ⁻¹ ) and low-light (300 µmol m –2 s ⁻¹ ) treatments was done to learn how H. suaveolens partitions its biomass between aboveground and belowground plant parts.
Results
The field study indicated that the sunlit areas had a higher abundance of H. suaveolens and a lower diversity of resident herbaceous species than the shaded areas. The common garden experiment showed that sun-dwelling H. suaveolens individuals performed better in germinative, vegetative, eco-physiological, and reproductive traits than the shade-dwelling individuals. The growth chamber experiment exhibited that plants grown in high-light environment had greater seed germination, seedling recruitment, and aboveground biomass than those grown in low-light environment, whereas plants grown in low-light environment exhibited a higher root mass ratio than the high-light individuals. These results suggest that H. suaveolens individuals mask the understory vegetation owing to higher seedling recruitment, relative growth rate, photosynthetic performance, resource acquisition-allocation, and reproductive output in response to high-light conditions.
Conclusions
The study concludes that light significantly controls the invasive population dynamics of H. suaveolens in dry deciduous forests. In high-light areas, H. suaveolens populations dominate the forest understory with suboptimal shade tolerance. In shade environment, H. suaveolens maintains a persistent soil seed bank along with ‘Oskar individuals’ that become active in response to high-light availability. The modus operandi is a ‘sit and wait’ strategy. The current study provides insights on prioritizing areas for H. suaveolens management that will potentially reduce the risk of biological invasions on the native species diversity of tropical regions.
... Resources and niches are well established but difficult to quantify concepts since they could potentially reflect an almost infinite number of factors. Thus, examinations of these hypotheses rely most often on proxies like the composition and diversity of the landscape as a potential path for decreasing interspecific competition and allowing the establishment and propagation of alien species [9][10][11]. ...
Biological invasions are one of the main threats to biodiversity, but they also offer insights on different ecological processes, as highlighted by the hypotheses posited to explain the phenomenon. We explore the relative importance of different hypotheses using biotic (native diversity) and abiotic factors (climate and landscape configuration) as proxies driving the spatial pattern of alien plant biodiversity in Greece. The strongest predictor of alien species richness is native species richness. Landscape heterogeneity boosts this relationship, but native and alien species prefer different conditions. Landscape composition and configuration explain more of the variance of alien diversity than of native diversity, with native diversity increasing at more naturally vegetated areas and alien diversity at agricultural lands. Climate is associated more strongly with native diversity than with alien diversity, with native diversity increasing in colder regions and alien diversity in warmer regions. The transportation network was associated with higher alien species richness but not with native species richness, highlighting the importance of propagule/colonization pressure. These differences might indicate that aliens occupy part of the niche space that is not preferred by the natives and thus allow us to speculate on the role of limiting similarity as a driving force.
... S.M. Haq et al. weed control and eradication plans in the region's rice agro-ecosystems. Such research is critical for managing weeds, as a lack of data on weeds is one of the most significant barriers to developing an effective weed management strategy [53]. ...
Field-based evaluation of indicator weed species in agriculture systems is effective for developing site-specific weed management. Because of anthropogenic activities and climate change, new weeds are invading agricultural fields. The current study examined the weed species in agricultural fields with a focus on diversity, distribution ecological patterns, and identification of indicator species, all of which might help in effective weed management practices. Following the calculation of the Importance Value Index for each weed species, ordination, and indicator species analysis were conducted. We collected vegetation data through random sampling, and the current study recorded 47 species, taxonomically distributed across 41 genera and 19 families. Asteraceae with 13% of species was the leading family followed by Poaceae, and Brassicaceae (11% each). Annual weed species with 49% were the dominant life span category. The phytogeographical analysis revealed that the highest number (28 species) recorded from the study area were native, while 19 species were alien. Of the total alien species, 4 were invasive, and 14 were naturalised and one species Bassia scoparia was casual. The diversity indices show significant differences (p ≤ 0.05) in weeds in rice fields recorded from the study area. Detrended correspondence analysis grouped the species Geranium rotundifolium, Sisymbrium loeselii, Mentha longifolia, Rumex dentatus, Amaranthus viridis on the extreme left side suggest a low score on axis 1 and a high score on axis 2. The analysis of indicator species revealed a significant difference (p ≤ 0.05) in agriculture fields across the sampled sites. According to the indicator species analysis, Poa annua, Persicaria hydropiper, Ranunculus sceleratus, and Echinochloa colona were the key weed species found in the study area. This investigation might aid weed management strategies to minimize crop losses and increase of rice production to fulfill the current food requirement.
Keywords
Plant traitsInvasive speciesIndicator speciesWeedInvasive plantBiodiversity
... Evaluation of temporally dynamic abiotic and biotic processes is crucial for planning to manage and protect ecosystems and biodiversity with reference to threats from invasive species (Zurell et al. 2022). The spatial variability of topography, pattern of different land uses, current species composition, ecosystem features, and species physiology, all impact the establishment of invasive species (Khuroo et al. 2009;Kumar et al. 2019). The taxonomic and geographic parameters influencing the dynamics of invasive species are poorly understood (Jamil et al. 2022). ...
The evaluation of alien invasive plants (AIPs) environmental and ecological distribution patterns can provide important data as well as a theoretical approach for management methods such as invasion prevention, eradi-cation, and control of established AIPs, and recognition of ecosystems at high threat of invasion. Twenty 100 m 2 transects were randomly established at each of the ten sample sites to collect data on the impact of edaphic properties on the spread of alien species in the semi-arid region. After computing the Important Value Index for each recorded species, ordination and indicator species analyses were carried out. In the study area, 104 alien plants from 30 families and 72 genera were found. Poaceae was the largest family, with 18 taxa (17%), followed by Fabaceae (14 taxa; 13%) and Asteraceae (11 taxa; 10%). Herbs made up 51% of these species, grasses 17%, and shrubs 12%. In terms of life form, Therophytes (56%) were dominant, followed by Chamaephytes (12%). The leaf size spectrum of alien plants was dominated by microphylls (48%), followed by mesophylls (23%), and nanophylls (20%). The CCA ordination analysis clearly showed that environmental variables like pH, moisture and soil texture (silt, sand, and clay) significantly (p ≤ 0.002) influenced how species are distributed in the region. In total, twenty indicator alien species were detected across five different semi-arid ecosystems. Par-thenium hysterophorus recorded from all ecotypes except wetland with highest IVI from agroecosystem. Prosopis juliflora was observed in urban ecosystems, while Stellaria media was in both agroecosystems and forests, and Lantana camara was only observed in forests. Our findings reveal patterns and processes of alien invasive plant in different ecotypes, but only remediation and continuous monitoring might affect the situation in the near future. In this regard, the baseline data gathered during the current study would be significant in guiding future scientific monitoring of such ecological changes, as well as in landscape ecology management and planning.
... This provides an opportunity to investigate the weed invasion in these two different land use classes. At the same time, the information on the phenological events of weed is important for formulating effective control and management [13,14]. ...
Weeds are a major threat to agriculture and horticulture cropping systems that reduce yield. Weeds have a better ability to compete for resources compared to the main crops of various agro-ecosystems and act as a major impediment in reducing overall yield. They often act as energy drains in the managed agroecosystems. We studied weed infestation for five different agro-ecosystems in the part of Indian Western Himalayas represented by paddy, maize, mustard, apple and vegetable orchards. Systematic random sampling was done to record flowering phenology and diversity of weeds during the assessment period 2015-2020. We recorded 59 weed species, taxonomically distributed among 50 genera in 24 families. The Asteraceae family has the most species (15% species), followed by Poaceae (14% species), and Brassicaceae (12% species). The Therophytes were the dominant life form followed by Hemicryptophytes. The majority of the weeds were shown to be at their most blooming in the summer (predominantly from June to July). The Shannon index based diversity of weeds ranged from 2.307-3.325 for the different agro-ecosystems. The highest number of weeds was in the horticulture systems (apple > vegetable) followed by agriculture fields (maize > paddy > mustard). Agriculture and horticulture cropping systems were distinguished using indicator species analysis, which was supported by high and significant indicator values for a number of species. Persicaria hydropiper, Cynodon dactylon, Poa annua, Stellaria media, and Rorippa palustris had the highest indicator value in agriculture cropping systems, while Trifolium repens, Phleum pratense, and Trifolium pratense had the highest indicator value in horticulture cropping systems. We found that eleven weed species were unique to apple gardens followed by nine in maize, four in vegetables, two in mustard and one in paddy fields. Spatial turnover (βsim) and nestedness-resultant components (βsne) of species dissimilarity revealed dissimilarity lower than 50% among the five cropping systems. The study is expected to assist in formulating an appropriate management strategy for the control of weed infestation in the study region.
... Field-based observations are usually time-consuming and costly and may not be easy for difficult terrain such as mountains (Banko 1998;Martin et al. 1998). Remote sensing has widely been used in vegetation mapping using multiple approaches and using different data sets (Clerici et al. 2012;Han et al. 2004;Jin et al. 2016Jin et al. , 2017Kumar et al. 2019aKumar et al. , 2021Singh et al. 2020a, b;Stibig et al. 2003;Mishra et al. 2021). With remote sensingbased observations, it is easy to trace the spatial and temporal changes in a forested landscape as observations are available at planetary scales with repeated observations ranging from days to months. ...
Classification of vegetation into appropriate classes is important for management and conservation planning. Field-based observations are now extensively supported with remote sensing-based observations for such classifications. We demonstrate here the application of a machine learning technique using the random forest (RF) to classify Landsat imageries in the mountainous terrain of the Indian Western Himalayas. The region represents a mega-diverse area having a wide variation in climate and vegetation types with a varied topography. In mountainous regions, vegetation classification is crucial to identify the natural resources for its conservation and management planning. Normalized difference vegetation index (NDVI) using near infra-red and red bands was created for the period 2013–2019. As the imageries are available at a temporal resolution of 16 days, a Fourier transformation was done to compress a large amount of data. To achieve a better accuracy of classification, topographic variables of elevation and slope together with climate variables of temperature and precipitation were considered while implementing the classification algorithm. We successfully characterized the mountainous terrain into the classes of non-forest, evergreen needle leaf trees, evergreen broadleaf trees, moist deciduous trees, dry deciduous trees, shrub, and agriculture with an overall accuracy of 80%. We compared the classified maps with existing vegetation type maps to see inconsistency in mapping with the demonstrated approach. The methodology demonstrated in this study can be used for classifying the landscape into distinct classes with improved accuracy for various purposes.
... Remote sensing satellite data and modelling techniques are valuable tools to infer ecosystem structure and processes' spatial and temporal patterns. Several researchers used these tools for vegetation characterisation (Kimothi and Dasari 2010;Munsi et al. 2010;Padalia et al. 2013;Srinet et al. 2020a;Mishra et al. 2021), forest dynamics (Joshi et al. 2011Munsi et al. 2012;Areendran et al. 2017;Kumar et al. 2021), species distribution modelling (Yang et al. 2013;Lamsal et al. 2018), spatial heterogeneity (Kumar et al. 2019), atmospheric CO 2 dynamics (Watham et al. 2021), gross primary productivity modelling (Mishra and Chaudhuri 2015;Srinet et al. 2020b), and foliar nutrients modelling (Vasudeva et al. 2021) in the Indian Siwalik region. ...
... MaxEnt modelling was used to predict the distribution of Justicia adhatoda L. (Yang et al. 2013) and some invasive plants (Lamsal et al. 2018). Further, it has been suggested that the spread of invasive plants is affected by spatial heterogeneity (Kumar et al. 2019). ...
The Himalayan foothills or the Siwaliks have been considered as one of the most fragile ecosystems of India. The underlying cause of degradation includes natural settings (geological formation and climate change) and human-mediated pressures (sandstone mining, urbanisation and developmental activities). Since the Siwaliks is associated with the livelihood of about 20 million people, it becomes imperative to respond appropriately for sustaining this ecologically fragile region. Ecological principles have great potential to develop sustainable practices for the conservation and management of natural resources. These principles offer nature-based solutions to sustain the ecologically sensitive and degraded ecosystems such as the ‘Siwaliks’. However, there is a need for concerted research to develop data-driven decisions for effective management. Therefore, a systematic analysis was conducted to assess the current patterns and knowledge gaps about plant ecological studies in the Indian Siwalik region. This article analysed the available peer-reviewed literature using established guidelines and bibliometric analyses. We found an increasing trend in the scientific output, and most studies were concentrated in the Siwalik region belonging to the Indian state of Uttarakhand. Although studies have been conducted on plant ecology, landscape ecology and ecosystem ecology have been disproportionately focused. Our findings suggest a growing interest in the plant ecology of the region; however, these studies seem relatively small compared to the complexity and diversity of this ecosystem structural and functional attributes. Further, with advances in computer application and remote sensing satellite data availability, we observed a shift towards ecological modelling studies, though experimental evidence also needs to be addressed.
... These effects differ for different forest age classes and are subjective trait differences of native and nonnative plant species (Dillon et al. 2018). Invasive species are able to affect local biodiversity on multiple scales and diverse ways (Kumar et al. 2019c). In natural forest ecosystems, risks due to IAS comprise of hybridization, disease transmission, and competition among species (Langmaier and Lapin 2020). ...
Forest ecosystems are one of the most important ecosystems on earth, and
sustainability of the planet heavily relies on diverse ecosystem services emerging
from them. In order to maintain unrestricted flow of ecosystem services in the
warming world, it is essential to conserve and sustainably manage them. This
necessitates an understanding of past, present, and future structural and functional
pattern of forests, their functioning as well as health status. There are substantial
indications that unsustainable human activities have significantly affected the
structure and functioning of natural forest ecosystems. To explain the distribution
of forests, their functioning, and different drivers of loss to which forests are
exposed, enormous methodological and socio-ecological and governance
advancements have already taken place. In the opening chapter, we elaborate
on the understanding of forest ecosystems from variations in definitions and
conceptualizations of forests, emerging challenges, monitoring advancements,
etc. Chapter broadly covers scientific advancements for monitoring various
stressors, forest degradation, inventory using advanced tools to present the fate
of forest structure, and functioning in the changing world. The volume highlights
drivers of deforestation and forest degradation, provides insights to innovations,
and also touches advanced institutional provisions and governance framework. The thematic and cross cutting chapters bring in scientific evidence-supported
information and solutions to enhance the prospects for conserving forests in the
fast changing world. Apart from providing a broader overview of the book, its
growing relevance, the chapter also offers a brief outline of the chapters in
different sections of the book.
... This helps to ensure sustainable development and understanding of human activities' effect within and around protected areas. Geospatial data such as aerial and satellite photographs can be used to manage flora and fauna by determining the presence and distribution of vegetation and invasive species within a protected area (Kumar et al. 2019b). It helps in determining the extent of vegetation, water and food availability for animals in different seasons of the year. ...
Geospatial assessment supported by the Geographic Information System (GIS), Remote
Sensing (RS), and Global Positioning System (GPS) caters for compelling techniques of
mapping, monitoring, surveying, classification, characterization, and change detection of
natural resources. These techniques provide a platform for generating valuable data, creating cartographic products, and performing timely analysis to make sound sustainable development decisions. Remote sensing involves the recording of information distantly without coming in contact with the object using the various electromagnetic spectrum. It employs the use of cameras, lasers, scanners, and specialized sensors that are located on the ground or aerial platforms (Jensen and Im 2007). The principle geospatial components of a study are derived using various methods such as aerial photographs, satellite imaging, Light Detection and Ranging (LiDAR) data, Unmanned Aerial Systems (UAS)/Drone data, GPS survey, etc., based on the study’s objective. Figure 2.1 displays different remote sensing components for collecting NRM data. Remotely sensed data captured through different satellites and other platforms such as drones platforms has wide applications in natural resource management disciplines. Multiple data from various sources also serve as input for other environmental models (Melesse and Graham 2004). The combined use of GIS,
remote sensing data, and GPS has enabled researchers and natural resource managers to
establish management plans for various applications (Philipson et al. 2003). The rest of this chapter will focus on multiple geospatial technologies and their application in different natural resource management areas.
... Any new approach that serves the purpose of classifying the plants into PFTs would help modellers to obtain global maps of PFT which is a pre-requisite for use and development of vegetation models for climate change impact studies (Foley et al., 1996;Kumar et al., 2018aKumar et al., , 2020Prentice, 1989;Quillet et al., 2010;Rawat et al., 2020;. The recent concerns of climate change impacts manifested on forest and agriculture have augmented the efforts of using new approaches and vegetation models for the vulnerability assessment of different ecosystems (Franklin et al., 2013;Gupta et al., 2020;Kalra and Kumar, 2019;Kraft et al., 2008;Kumar et al., 2018bKumar et al., , 2019aKumar et al., , 2019bKumar et al., , 2019cLevis et al., 2017;Pandey et al., 2018;Peng, 2000;Pokhriyal et al., 2020;Rawat et al., 2020;. Different Dynamic Global Vegetation Models (DGVMs) have evolved in recent times and these models are capable of simulating transient patterns of vegetation under the influence of climate change which is much in use due to global concern of climate change (Gillison, 2019;Peng, 2000). ...
Phenological studies involve capturing information on dates of recurrent and seasonal biological events in plants
and animals. In plants, phenological events of leaf flushing, full bloom, autumn discolouration, leaf fall etc. can
be used to distinguish vegetation into separate classes. We refer here such phenological distinction as Phenological
Functional Types (PhFT). The PhFT can be considered as the precursor of Plant Functional Types (PFTs)
where PFT uses additional traits (e.g. leaf area, tree height, leaf structure, rate of evapotranspiration and photosynthesis,
etc.) to classify vegetation. The PFT classification is essentially needed for developing and running dynamic
global vegetation models (DGVMs) used in studying impacts of climate change on vegetation. We used
archived long time series satellite remote sensing data, Landsat 5,7 and 8 (1985–2019), for classifying a landscape
into appropriate PhFT. Normalized Difference Vegetation Index (NDVI) was calculated for the given period
in the Google Earth Engine (GEE). The GEE is a cloud-based platform developed for the retrieval and processing
of remotely sensed images and other data. The monthly median values of NDVI for the mentioned period was
used to label each pixel into appropriate PhFT classes using Random Forest (RF) algorithm in GEE to obtain four
distinct classes of evergreen forest, deciduous forest, agriculture and non-forest. The comparison of PhFT map
was done with reference maps of global MCD12Q1 and the forest type map of India; with an overall moderate
agreement of 68.55% and 66.22%, respectively. MCD12Q1 has an accuracy of 73% for the land cover map and
the forest type map of India has 75% accuracy, whereas, we achieved an overall accuracy of 78%. The PhFT classification
accuracy can further be improved using additional indices and topographic variables. The methodology
demonstrated in this study can be adopted for classifying a landscape into distinct PhFT/PFT classes.
