Herbaceous Wetland Research Articles

The feasibility of using national‐scale datasets for classifying wetlands in Arizona with machine learning

AbstractThe advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national‐scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland‐specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non‐wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

Imaging Spectroscopy‐Based Estimation of Aboveground Biomass in Louisiana's Coastal Wetlands: Toward Consistent Spectroscopic Retrievals Across Atmospheric States

AbstractDeveloping accurate landscape‐scale aboveground biomass (AGB) maps is critical to understanding coastal deltaic wetland resilience, as AGB influences stability and elevation dynamics in herbaceous wetlands. Here we used AVIRIS‐NG imaging spectrometer (or “hyperspectral”) data from NASA's 2021 Delta‐X mission in coastal Louisiana to map seasonal changes in herbaceous AGB across two deltaic basins with contrasting sediment delivery and hydrologic regimes: the Atchafalaya (active) and Terrebonne (inactive). We assessed the impact of atmospheric effects on our retrievals, as high water vapor content in August 2021 caused significant noise in the 880–1,000 and 1,080–1,200 nm near‐infrared (NIR) wavelengths. We hypothesized that correcting these wavelengths with our conditional Gaussian interpolation algorithm would improve AGB estimates due to their association with plant canopy water content. We empirically assessed the performance of the corrected spectra on AGB estimates using Partial Least Squares Regression (PLSR), finding that the corrected NIR bands attained high variable importance and reduced estimation errors. Our Random Forest regression approach based on the corrected spectra attained equivalent error metrics via leave‐one‐out‐cross‐validation as the PLSR models (R2 = 0.43, mean absolute error = 257.3 g/m2) while greatly improving the AGB maps' visual quality, having better captured variability while reducing noise and discontinuities in AGB estimates across flightlines. The maps show differing seasonal growth, with the Atchafalaya and Terrebonne Basins' AGB increasing from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively. We demonstrated that imaging spectroscopy can be applied to assess herbaceous biomass stocks, growth patterns, and resilience in coastal ecosystems.

Direct and indirect effects of an invasive ecosystem engineer on a rare herbaceous wetland

Direct and indirect effects of an invasive ecosystem engineer on a rare herbaceous wetland

Forecasting Land Use Dynamics in Talas District, Kazakhstan, Using Landsat Data and the Google Earth Engine (GEE) Platform

This study employs the robust capabilities of Google Earth Engine (GEE) to analyze and forecast land cover and land use changes in the Talas District, situated within the Zhambyl region of Kazakhstan, for a period spanning from 2000 to 2030. The methodology involves thorough image selection, data filtering, and classification using a Random Forest algorithm based on Landsat imagery. This study identifies significant shifts in land cover classes such as herbaceous wetlands, bare vegetation, shrublands, solonchak, water bodies, and grasslands. A detailed accuracy assessment validates the classification model. The forecast for 2030 reveals dynamic trends, including the decline of herbaceous wetlands, a reversal in bare vegetation, and concerns over water bodies. The 2030 forecast shows dynamic trends, including a projected 334.023 km2 of herbaceous wetlands, 2271.41 km2 of bare vegetation, and a notable reduction in water bodies to 24.0129 km2. In quantifying overall trends, this study observes a decline in herbaceous wetlands, bare vegetation, and approximately 67% fewer water bodies from 2000 to 2030, alongside a rise in grassland areas, highlighting dynamic land cover changes. This research underscores the need for continuous monitoring and research to guide sustainable land use planning and conservation in the Talas District and similar areas.

Root and shoot phenology, architecture, and organ properties: an integrated trait network among 44 herbaceous wetland species.

Integrating traits across above- and belowground organs offers comprehensive insights into plant ecology, but their various functions also increase model complexity. This study aimed to illuminate the interspecific pattern of whole-plant trait correlations through a network lens, including a detailed analysis of the root system. Using a network algorithm that allows individual traits to belong to multiple modules, we characterize interrelations among 19 traits, spanning both shoot and root phenology, architecture, morphology, and tissue properties of 44 species, mostly herbaceous monocots from Northern Ontario wetlands, grown in a common garden. The resulting trait network shows three distinct yet partially overlapping modules. Two major trait modules indicate constraints of plant size and form, and resource economics, respectively. These modules highlight the interdependence between shoot size, root architecture and porosity, and a shoot-root coordination in phenology and dry-matter content. A third module depicts leaf biomechanical adaptations specific to wetland graminoids. All three modules overlap on shoot height, suggesting multifaceted constraints of plant stature. In the network, individual-level traits showed significantly higher centrality than tissue-level traits do, demonstrating a hierarchical trait integration. The presented whole-plant, integrated network suggests that trait covariation is essentially function-driven rather than organ-specific.

Multifaceted impacts of Ukraine’s Kakhovka Dam destruction

This study presents a rapid, spatially precise, and comprehensive assessment of the far-reaching impact resulting from the recent demolition of the Kakhovka Dam in Ukraine, leveraging multi-source high-resolution satellite imagery and advanced remote sensing techniques. The findings highlight the extensive downstream flooding (742.58 km2), which includes approximately 62,303 people exposed and 2,361 buildings submerged, with increased effects in buffer zones, and significant damage to 539.84 km2 of herbaceous wetlands in the Dnipro River area. Moreover, we observed a substantial reduction in water storage upstream (177.53 billion m3, approximately 97% of the reservoir’s capacity). Our estimates underscore the potential threat to downstream water supplies and the functioning of the Zaporizhzhia plant, the largest nuclear plant in Europe. Our research emphasizes the immediate need for coordinated action to manage the unfolding crisis and sets an example for future studies that use similar methodologies in conflict regions to enable rapid and effective responses.

Estimation of the Ecosystem Service Value of the Yellow River Delta-Laizhou Bay Coastal Zone Considering Regional Differences and Social Development.

With economic and societal development, the ecological environment of the Yellow River Delta-Laizhou Bay coastal zone has been seriously damaged. Exploring the changes in land use and ecosystem service value (ESV) is essential to ecological construction of the region. The random forest classification method was used for land cover interpretation of the four periods of remote sensing images in the study area from 1990 to 2020. Newly calculated regional difference coefficients and social development coefficients were used to construct a dynamic ESV assessment model and to study its changes from overall and sea‒land gradient perspectives. The results showed that construction land, salt pans, aquaculture ponds, and inland water masses expanded rapidly, while cropland, tidal flats, and shallow waters shrank sharply over the past 30 years. The ESV in the study area has continued to decrease from 34.47 billion yuan in 1990 to 25.23 billion yuan in 2020, a total decrease of 9.23 billion yuan. This is mostly due to the encroachment of construction land, salt pans, and aquaculture ponds, and the flow of ecosystem services from high-value land cover types (tidal flats, herbaceous wetlands, and cropland) to medium- and low-value land cover types. Moreover, the land cover transfer and ESVs exhibited a decreasing trend from sea to land, with significant sea-land gradient differences. Land conversion is most common in the 0-15 km coastal zone, mainly from natural wetlands to artificial wetlands, where the ESV also decreases rapidly. Considering the regional differences and social development in this paper, the ESV of small-scale areas can be reasonably evaluated to explore the characteristics and causes of changes in land use and ESVs, which can provide an important reference for ecological protection and land use management in the region.

Eco-epidemiology of Rickettsia amblyommatis and Rickettsia parkeri in naturally infected ticks (Acari: Ixodida) from South Carolina

BackgroundSpotted fever group Rickettsia (SFGR) is the largest group of Rickettsia species of clinical and veterinary importance emerging worldwide. Historically, SFGR cases were linked to Rickettsia rickettsii, the causal agent of Rocky Mountain spotted fever; however, recently discovered species Rickettsia parkeri and Rickettsia amblyommatis have been shown to cause a wide range of clinical symptoms. The role of R. amblyommatis in SFGR eco-epidemiology and the possible public health implications remain unknown.MethodsThis study evaluated statewide tick surveillance and land-use classification data to define the eco-epidemiological relationships between R. amblyommatis and R. parkeri among questing and feeding ticks collected across South Carolina between 2021 and 2022. Questing ticks from state parks and feeding ticks from animal shelters were evaluated for R. parkeri and R. amblyommatis using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) on pooled samples. A Bayesian multivariable logistic regression model for pool testing data was used to assess associations between R. parkeri or R. amblyommatis infection and land-use classification variables among questing ticks. The Spearman correlation was used to evaluate the relationship between the two tested pathogens.ResultsThe infection prevalence for R. amblyommatis was 24.8% (23.4–26.3%) among questing ticks, and 39.5% (37.4–42.0%) among feeding ticks; conversely, for R. parkeri it was 19.0% (17.6–20.5%) among questing ticks and 22.4% (20.3–24.5%) among feeding ticks. A negative, refractory correlation was found between the species, with ticks significantly more likely to contain one or the other pathogen, but not both simultaneously. The Bayesian analysis revealed that R. amblyommatis infection was positively associated with deciduous, evergreen, and mixed forests, and negatively associated with hay and pasture fields, and emergent herbaceous wetlands. Rickettsia parkeri infection was positively associated with deciduous, mixed, and evergreen forests, herbaceous vegetation, cultivated cropland, woody wetlands, and emergent herbaceous wetlands, and negatively associated with hay and pasture fields.ConclusionsThis is the first study to evaluate the eco-epidemiological factors driving tick pathogenicity in South Carolina. The negative interactions between SFGR species suggest the possible inhibition between the two pathogens tested, which could have important public health implications. Moreover, land-use classification factors revealed environments associated with tick pathogenicity, highlighting the need for tick vector control in these areas.Graphical

Influences of landscape composition on hunter‐harvested mallard body mass and condition in eastern Arkansas

AbstractWaterfowl with more body mass and a greater body condition during the non‐breeding season are thought to be more likely to survive and have increased productivity during the following breeding season. Body mass and body condition in waterfowl should reflect the resources available to them locally. We analyzed the relationship of landscape composition on mallard (Anas platyrhynchos) body mass and body condition (mass‐wing length index) among age and sex groups. We calculated these variables from hunter‐harvested mallards during the 2019–2020 and 2020–2021 duck hunting seasons in the Lower Mississippi Alluvial Valley of Arkansas, USA. We used linear mixed‐effects models to analyze changes in body mass and body condition with changes in the percent landscape composition of water cover, woody wetlands, herbaceous wetlands, rice, soybeans, and disturbance. We found that body mass and condition of harvested mallards were positively associated with greater proportions of water cover and woody wetlands but negatively associated with greater proportions of herbaceous wetlands and human disturbance from human infrastructure. Management actions focused on providing flooded and woody wetland areas on the landscape that allow waterfowl to access food resources, while decreasing the disturbance around wetlands in the form of road density and human infrastructure, should increase body mass and body condition in mallards spending the non‐breeding season in the Lower Mississippi Alluvial Valley.

Opportunities for Monitoring Soil and Land Development to Support United Nations (UN) Sustainable Development Goals (SDGs): A Case Study of the United States of America (USA)

Land, including soil resources, makes important contributions to the United Nations (UN) Sustainable Development Goals (SDGs). However, there are challenges in identifying land/soil measurable information (e.g., indicators, metrics, etc.) to monitor the progress toward achieving these goals. This study examines the role of land/soil in selected SDGs (SDG 2: Zero Hunger; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action; SDG 15: Life on Land) and provides practical examples on how to use geospatial analysis to track relevant qualitative and quantitative land/soil data using the contiguous United States of America (USA) as a case study. The innovative aspect of this study leverages geospatial technologies to track the intersection of land use/land cover (LULC) change and soil resources to quantify development trends within the overall land cover matrix to evaluate if these trends are sustainable. Classified land cover data derived from satellite-based remote sensing were used to identify the extent of developed areas in 2016 and the change in development areas since 2011. Most land development through time in the USA has caused losses (area loss of nearly 355,600 km2, with projected midpoint losses of about 5.7 × 1012 kg total soil carbon (TSC) and about $969B (where B = billion = 109, USD) in social costs of carbon dioxide emissions, SC-CO2). All ten soil orders present in the contiguous USA experienced losses from developments, which represents a loss for both biodiversity and soil diversity (pedodiversity). The contiguous USA experienced an increase in land/soil consumption between 2001 and 2016 at the expense of deciduous forest (−3.1%), evergreen forest (−3.0%), emergent herbaceous wetlands (−0.6%), and hay/pasture (−7.9%). These “new” land developments (24,292.2 km2) caused a complete projected midpoint loss of 4.0 × 1011 kg TSC, equivalent to $76.1B SC-CO2. States with the largest developed areas and the highest TSC losses with associated SC-CO2 were Texas and Florida. The proposed methodology used in this study can be applied worldwide, at various spatial scales, to help monitor SDGs over time. With improved tools to monitor SDGs, progress on these SDGs may require linking the SDGs to existing or future international and national legal frameworks.

Coastal wetland degradation and ecosystem service value change in the Yellow River Delta, China

The characteristics of changes in wetlands in different coastal areas vary, so it is necessary to select representative experimental areas to research wetland degradation, ecosystem service values (ESVs) and differences in changes among various wetland types in different ecological service function fields. The goal of this study is to better realize the spatiotemporal characteristics of wetland degradation and ESV in order to balance economic advancement and wetland ecological protection. The evolution of the wetland ESVs was analyzed by using the ESV equivalent correction technique and ecological contribution degree. The random forest (RF) classification model was utilized to extract wetland information in the Yellow River Delta (YRD) in four years (1990, 2000, 2010, and 2020). Thoroughly exploring the mechanism of impact and proposing reasonable suggestions based on a close combination of wetland changes and ecological services are conducive to the healthy development of global wetlands. The results demonstrated that the total area of wetlands expanded from 1459.5 km2 in 1990–1975.5 km2 in 2020, an increase of 28.5 %, with artificial wetlands increasing from 151.4 km2 to 1059.1 km2 and natural wetlands decreasing from 1308.1 km2 to 916.5 km2. The wetland ESV increased from 18.4 billion USD in 1990–28.6 billion USD in 2020. The transformation between types of natural wetlands tended to increase the ESV, while the transformation from natural wetlands to constructed wetlands reduced the ESV. The tidal flats and herbaceous wetlands have great value in terms of not only hydrological regulation but also gene diversity and climate regulation. The significant changes in various wetlands cause changes in different ecological service functions and promote changes in the ESV. Wetland protection, restoration, reconstruction, and rational utilization must be incorporated into urban land planning, and wetland parks and wetland protection zones should be established according to local conditions. This above research can provide typical examples and references for global wetland protection and sustainable development.

Identity and diversity of pollens collected by two managed bee species while in blueberry fields for pollination.

The nutritional needs and foraging behavior of managed bees often lead to pollen collection from flowers other than the focal crop during crop pollination. To understand the pollen needs and preferences of managed bees during blueberry pollination, we identified pollen collected by Apis mellifera Linnaeus, 1758 (Hymenoptera: Apidae) and Bombus impatiens Cresson, 1863 (Hymenoptera: Apidae) colonies across two years. Bumble bees collected a wider diversity of pollens compared to honey bees, whereas honey bees were more focused on abundant resources. Despite blueberries being the most abundant resource in the landscape, it was not the most collected pollen by either bee species in 2018. However, it was the most collected pollen by bumble bees in 2019 and they collected substantially more blueberry pollen than honey bees in both years. In 2018, buckthorn, Rhamnus L. (Rosales: Rhamnaceae) or Frangula Mill. (Rosales: Rhamnaceae), and willow, Salix L. (Malpighiales: Salicaceae), pollens were abundantly collected by both bee species. In 2019, cherry, Prunus L. (Rosales: Rosaceae), and willow (Salix) pollens were collected at high proportions by both species. Brambles, Rubus L. (Rosales: Rosaceae), and white clover, Trifolium repens L. (Fabales: Fabaceae), were also common pollen sources for honey bees, whereas oak, Quercus L. (Fagales: Fagaceae), was collected by bumble bees. Landscape analyses also revealed that certain land cover types were positively correlated with the collection of preferred pollen types. Herbaceous wetlands were associated with collection of buckthorn (Rhamnus/Frangula), willow (Salix), and cherry (Prunus) pollen, which were primary pollen resources for both bee species. There was no correlation between landscape diversity and pollen diversity, suggesting that colonies forage based on nutritional requirements rather than resource availability.

Identifying driving hydrogeomorphic factors of coastal wetland downgrading using random forest classification models

Coastal wetlands provide critical ecosystem services but are experiencing disruptions caused by inundation and saltwater intrusion under intensified climate change, sea-level rise, and anthropogenic activities. Recent studies have shown that these disturbances downgraded coastal wetlands mainly through affecting their hydrological processes. However, research on what is the most critical driver for wetland downgrading and how it affects coastal wetlands is still in its infancy. This study examined drivers of three types of wetland downgrading, including woody wetland loss, emergent herbaceous wetland loss, and woody wetlands converting to emergent herbaceous wetlands. By using random forest classification models for the wetland ecosystems in the Alligator River National Wildlife Refuge, North Carolina, USA, during 1995–2019, we determined the relative importance of different hydrogeomorphic processes and the dominant variables in driving the wetland downgrading. Results showed that random forest classification models were accurate (> 97 % overall accuracy) in classifying wetland downgrading. Multiple hydrogeomorphic variables collectively contributed to the coastal wetland downgrading. However, the dominant control factors varied across different types of wetland downgrading. Woody wetlands were most susceptible to saltwater intrusion and were likely to downgrade if the saltwater table was shallower than 0.2 m below the land surface. In contrast, emergent herbaceous wetlands were most vulnerable to inundation and drought. The favorable groundwater table for emergent herbaceous wetlands was between 0.34 m above the land surface and 0.32 m below the land surface, beyond which the emergent herbaceous wetland tended to disappear. For downgraded woody wetlands, their distance to canals/ditches played a crucial role in determining their fates after downgrading. The machine learning approach employed in this study provided critical knowledge about the thresholds of hydrogeomorphic variables for the downgrading of different types of coastal wetlands. Such information can help guide effective and targeted coastal wetland conservation, management, and restoration measures.

Changes in Ecosystem Structure and Composition Influence Groundwater Chemistry in Herbaceous Wetlands

Changes in Ecosystem Structure and Composition Influence Groundwater Chemistry in Herbaceous Wetlands

CO-OCCURRENCE OF MOSQUITO COMMUNITIES IN DISTURBED ENVIRONMENTS USING MARKOV RANDOM FIELDS (MRFs) IN ST. JOHNS COUNTY, FLORIDA

The distribution of mosquito communities is predicted by complex micro- and macrohabitat systems. While macrohabitat variables are significant in modeling the distribution of individual mosquito species, the distribution of mosquito communities in disturbed urban and semi-urban environmental gradients was overlooked in most of the previous models. In our study, we used conditional Markov Random Fields (CRF) to evaluate spatial co-occurrence patterns between mosquito vectors of eastern equine encephalitis (EEEV) and west Nile virus (WNV) in a disturbed urban environment in Saint John’s County, Florida. We aimed to 1) quantify the strength and direction of spatial unconditional and conditional correlations between mosquito assemblages in disturbed environments, and 2) evaluate whether the strength of correlations between mosquito assemblages is conditional on landscape or climate variables. We leveraged the longitudinal surveillance effort using Biogents sentinel traps (BGS) conducted by Anastasia Mosquito Control Districts in disturbed urban environments during 2017-2020. The distribution of high mosquito abundance, especially Aedes albopictus, Ae. aegypti, Ae. vexans, Ae. taeniorhynchus, Culex nigripalpus, Cx. salinarius, and Cx. quinquefasciatus, were conditionally correlated with other EEEV and WNV vector species in reduced woody and herbaceous wetlands and evergreen forests (-54.44%), and in urban developed landscapes (3.44%) during 2019 and 2020. Moreover, conditional correlations between mosquito species pairs were positively associated with increased total precipitation and in areas with high average minimum and maximum temperatures. Our results show that the micro- and macrohabitat characteristics demonstrated spatial effects on distribution and correlations between species pairs of EEEV and WNV mosquito vectors across disturbed environments. Our findings could be used to better understand the joint effects of drivers on mosquito diversity at a specific locality, interspecific interactions among mosquito assemblages, and how this diversity changes across environmental gradients.

RadWet: An Improved and Transferable Mapping of Open Water and Inundated Vegetation Using Sentinel-1

Mapping the spatial and temporal dynamics of tropical herbaceous wetlands is vital for a wide range of applications. Inundated vegetation can account for over three-quarters of the total inundated area, yet widely used EO mapping approaches are limited to the detection of open water bodies. This paper presents a new wetland mapping approach, RadWet, that automatically defines open water and inundated vegetation training data using a novel mixture of radar, terrain, and optical imagery. Training data samples are then used to classify serial Sentinel-1 radar imagery using an ensemble machine learning classification routine, providing information on the spatial and temporal dynamics of inundation every 12 days at a resolution of 30 m. The approach was evaluated over the period 2017–2022, covering a range of conditions (dry season to wet season) for two sites: (1) the Barotseland Floodplain, Zambia (31,172 km2) and (2) the Upper Rupununi Wetlands in Guyana (11,745 km2). Good agreement was found at both sites using random stratified accuracy assessment data (n = 28,223) with a median overall accuracy of 89% in Barotseland and 80% in the Upper Rupununi, outperforming existing approaches. The results revealed fine-scale hydrological processes driving inundation patterns as well as temporal patterns in seasonal flood pulse timing and magnitude. Inundated vegetation dominated wet season wetland extent, accounting for a mean 80% of total inundation. RadWet offers a new way in which tropical wetlands can be routinely monitored and characterised. This can provide significant benefits for a range of application areas, including flood hazard management, wetland inventories, monitoring natural greenhouse gas emissions and disease vector control.

Metriocnemus erythranthei sp. nov. and Limnophyes viribus sp. nov. (Diptera: Chironomidae: Orthocladiinae): leafminers of monkeyflowers, speedwells, and other herbaceous plants, with new observations on the ecology and habitats of other leaf-mining Chironomidae

In this study, we describe Metriocnemus erythranthei sp. nov. and Limnophyes viribus sp. nov., leafminers of herbaceous wetland plants. The M. erythranthei larva is a true miner entering fresh leaves and excavating the tunnels, and the L. viribus larva inhabits vacated mines of M. erythranthei. M. erythranthei is widespread in North America, with collections from the Pacific coast to Pennsylvania, and L. viribus has been collected from Iowa and Oregon. We also describe the larva of a possible new species associated with these plants, which we refer to as Metriocnemus sp. "Oregon". A key to the known larval stages of North American Metriocnemus is also provided. Along with providing a detailed account of the mining ecology of these new species, we discuss additional observations of mostly Orthocladiinae midges associated with aquatic and terrestrial plants. These include documenting the rearing of Metriocnemus eurynotus (Holmgren, 1883) from larvae feeding on Impatiens (Balsaminaceae) cotyledons, initially as leafminers and later externally. Larvae of M. eurynotus also were found feeding within mines of M. erythranthei on Veronica (Plantaginaceae) and were collected along with M. erythranthei larvae on leaves of Petasites (Asteraceae).

Differences of Moss Mites Communities at Different Vegetation Succession Stages in Subalpine Wetland (Jiulongchi, Fanjing Mountain), Southwest China

Fanjing Mountain, China, is a World Man and Biosphere Reserve, a World Natural Heritage Property, and a Nature Reserve in China. Mites communities have been reported from the Fanjing Mountain. Wetland moss microhabitats provide a unique habitat for mites, and our objective is to document moss mites communities in the subalpine wetlands of Fanjing Mountain (Jiulongchi), with a particular focus on trends in moss mites at different stages of vegetation succession in the wetlands, which have rarely been described, and the research will provide fundamental data for biodiversity conservation in the face of global climate change. The succession sequence from bryophyte (hygrophyte) to shrub (mesophyte or xerophyte) in the open area of Jiulongchi wetland successively includes Polytrichum commune Hedw. (PC), Eleocharis yokoscensis (Franch. et Sav.) Tang et-Cypers sp. (EY-C), Senecio faberii Hemsl (SF), and Indocalamus longiauritus Hand.-Mazz. (IL) four typical communities. In April 2016, we collected moss samples under the four typical communities mentioned above, and analyzed the difference of moss mite communities using PCA, Kruskal–Wallis nonparametric test, and ANOVA. The results showed the following: (1) A total of 9058 moss mites belonging to 49 genera in 3 orders and 69 families were captured in the four plant communities, with the moss mites Parachipteria, Fuscozetes, and Tectocepheus being the dominant taxa of moss mites in Jiulongchi wetland. The core taxa of moss mites at different successional stages were 12 genera, with IL having the largest number of exclusive taxa (20 genera). (2) The abundance of moss mites showed an overall increasing trend from PC to IL habitats, with the number of families and genera showing a pattern of IL > EY-C > SF > PC. The diversity index SF habitat possessed the maximum value, followed by IL, both of which were significantly different from PC. (3) The results of PCA analysis of moss mites showed that the mite community composition of PC habitats differed more from SF and IL, respectively, and less from EY-C. (4) There existed differences in the ecological taxa of predatory moss mites at different successional stages, with the Poronota group dominating the ecological taxa of oribatida. The research indicated that moss mites communities in subalpine wetlands were rich in species composition, and with the succession of vegetation from hygrophyte to mesophyte or xerophyte, the moss mites group as a whole develops in a direction favorable to its diversity, and the differences in moss mites ecological taxa also demonstrated the variability and complexity of the Jiulongchi wetland environment. This research presents the distribution pattern of moss mites in different vegetation succession stages in subtropical subalpine herbaceous wetlands. The moss mites evolution trend in response to climate-change-induced plant community succession needs further investigation.

A Bayesian approach for multiscale modeling of the influence of seasonal and annual habitat variation on relative abundance of ring-necked pheasant roosters

Finding ecologically relevant relationships between environmental covariates and response variables requires determining appropriate scales of effect. While considering multiple spatial scales of effect in hierarchical models has been the focus of recent studies, the effect of spatiotemporal scales, and temporal resolution of data on habitat suitability and species abundance has received less attention. We investigated relationships between ring-necked pheasant rooster abundance and environmental covariates with the goal of identifying important variables and their scales of effect in South Dakota, U.S.A. Using a suite of remote sensing data, we examined whether seasonal environmental conditions influence pheasant relative abundance and how survey conditions might affect detectability of roosters. To select optimal scales of effect and the best subset of covariates simultaneously, we employed a Reversible-Jump Monte Carlo Markov Chain (RJMCMC) approach in a Bayesian framework. We explored sources of uncertainty in data and controlled them through consideration of random effects. The use of seasonal covariates in addition to annual covariates revealed differential effects on species abundance. The proportion of grasslands on the landscape was an important covariate in models in all years, with rooster abundance generally being highest at intermediate levels of grassland density at local scales of effect. Pheasant abundance was also positively related to the proportion of small grain crop cover on the landscape at >2 km scales. Spring gross primary productivity and percentage of herbaceous wetlands on the landscape, both at a large scale (8 km), were the most important covariates in the wet years of 2018 and 2019 and were positively related to pheasant abundance. Grasslands at intermediate levels of density explained variability of pheasant abundance. However, other variables important to pheasant relative abundance varied among years depending on prevailing weather and climate conditions. Our workflow to model relationships between relative abundance and habitat components for pheasants can also be employed to model count data for other species to inform management decisions.

USING RVI AND NDVI TIME SERIES FOR CROPLAND MAPPING WITH TIME-WEIGHTED DYNAMIC TIME WARPING

Abstract. Monitoring and management of agricultural lands are essential due to reasons affecting agriculture, such as increasing population and global climate. With the increase in the temporal resolution of satellite systems, time-series classifications have become popular in cropland mapping. Because annual plants can give similar spectral reflectance values on the same date. In this context, agricultural land (∼500 km2) was selected in the south of South Dakota in the United States. The area includes alfalfa, corn, soybeans, winter wheat plants, developed, grassland/pasture, herbaceous wetlands, and open water areas. The study aims to map croplands with vegetation indices produced by annual Sentinel-1 and Sentinel-2 satellites. In this context, Radar Vegetation Index (RVI) produced from 25 Sentinel-1, and the Normalized Difference Vegetation Index (NDVI) produced from 26 Sentinel-2 satellites were used for 2020. We used the Time-Weighted Dynamic Time Warping (TWDTW) algorithm, which separates and classifies the similarities between two time series with variable speeds with time constraints. For mapping, the indices were classified both individually and combined. The highest overall accuracy (77.2%) was obtained with the combined use of NDVI and RVI. Among the plant classes, the lowest accuracy (83.71%) was found, and it was determined that the plant classes did not mix much. Sentinel-2 satellite is not available before April due to weather conditions in the region. For this reason, since the Sentinel-1 satellite is not affected by weather conditions, it is thought that the use of two satellites together will be beneficial in time series analysis.