Inland Marshes Research Articles

A long-term (1984–2021) wetland classification dataset for the Yangtze River Basin from continuous Landsat image collections

The wetlands along the Yangtze River Basin (YRB) have provided a variety of ecological and economic benefits. However, the lack of a long-term wetland classification dataset with comprehensive wetland categories has created in a barrier to evaluating the long-term variations of YRB wetlands for their habitat health, carbon storage, greenhouse gas emissions, and ecosystem service capacity. Therefore, this study aimed to generate a wetland classification dataset for the YRB covering the time period from 1984 to 2021. The dataset named Long-Term Wetland Classification Dataset for YRB (LTWCD_YRB) was created using a Random Forest machine learning classifier on Google Earth Engine with 30 m resolution Landsat 5, 7, 8 muti-spectral images. The maps of LTWCD_YRB demonstrated the spatial distribution, annual variability, and seasonal cycle of nine wetland categories in the extent, and the total validation accuracy can reach 85 %. The LTWCD_YRB indicates that the total wetland area of the YRB in 2021 was larger than that in 1984, with a constant increase in human-made wetlands and fluctuating natural wetlands. Aquaculture ponds expanded the most by 4,987 km2, while inland marshes in the source region exhibited the most fluctuations. Seasonal changes in wetlands were prominent in the Poyang Lake Basin, Dongting Lake Basin, and YRB source region. Human activities were found to be more dominant than natural driving forces in affecting wetlands. The LTWCD_YRB offers a consistent agreement of wetland area variations with the other satellite-based wetland datasets in the YRB, making it valuable for researchers, policymakers, and stakeholders to better understand the YRB wetlands and to support sustainable wetland management practices.

A hybrid modelling approach for detecting seasonal variations in inland Green-Blue Ecosystems

Deforestation, environmental pollution, and the overexploitation of resources, in addition to the Earth's natural cycles, are scaling up the impacts of climate change in the provision of Ecosystem Services (ES). Green-Blue Ecosystems (GBE) are impacted by climatic conditions, topography, and water presence. Data-driven modelling techniques may effectively capture the effects of seasonal variations while modelling natural ecosystems. This research proposes a hybrid modelling approach that combines Deep Learning and traditional Machine Learning, Sensitivity Analysis and Feature Importance Evaluation (FIE) to investigate seasonality effects on mapping GBE. The models, built using satellite imagery from the Spring and Summer seasons of the Mediterranean climate zone, included spectral indices, topography (DEM), and groundwater depth (GD). The model that best suited the analysis was selected using sensitivity tests and hyperparameter optimization. The study shows that land cover classes of transitional woodland shrubs, inland marshes, cultivated land parcels, and watercourses are better classified in the Spring, with an accuracy of 0.814. FIE indicates that spectral indices are the most important predictors for detecting green ecosystems in both seasons. Additionally, DEM and GD are the most relevant predictors to classify watercourses in the Summer. An analytical examination of the input data and hyperparameter settings facilitates understanding of models' behaviour while improving models' prediction. This research provides an advanced understanding of the effects of seasonal variations on the status of GBE and enhances understanding of modelling ES in areas with a growing need for changes in land use and high water supply demand.

Effects of Mosaic Natural Conditions on the Tourism Management of a Lowland Water Reservoir, Lake Tisza, Hungary

The increasing number of visitors and conflicts resulting from shared use in valorising sensitive wetlands make effective visitor and site management measures essential. In the course of this research, the landscape pattern, the current spatial distribution of tourist activities and the possible arrangement of expedient further development were examined. The study area, Lake Tisza, is an artificial lowland reservoir established in the 1970s with an area of 127 km2 in Hungary. Among its original functions, in addition to flood control, nature conservation and recreation have become the main profiles. The diverse hydro-ecological features and mosaic landscape structure allow for nature conservation and utilisation in tourism. This differentiated use is in line with the worldwide trend of favouring locations with multiple leisure activities in close proximity to each other. Based on the CORINE Land Cover (CLC) 2018 database, 12 different land use categories were identified in the study area. The largest proportion is represented by water bodies (53.29%), while inland marshes and broad-leaved forests cover 22.25% and 16.64%, respectively. The heterogeneity of the area is considerably enhanced by the high patch sizes of the categories pastures, broad-leaved forests and inland marshes. According to the Shannon diversity index, the most complex landscape diversity can be found in the Tiszavalk (1.5) and Poroszló (1.4) basins, considered to be the most suitable for ecotourism, while the lower values of the Sarud (1.1) and Abadszalók (1.1) basins provide suitable conditions for water sport activities and recreational tourism. Continuous adaptation to social needs and the joint protection of natural values is crucial for the sustainable development of Lake Tisza.

Wetland mapping in East Asia by two-stage object-based Random Forest and hierarchical decision tree algorithms on Sentinel-1/2 images

Accurate information on wetland extent in East Asia is essential to assess progress towards Sustainable Development Goals (SDGs) and the use of wetland resources, where wetlands benefit a quarter of the world's population and millions of wild birds in over three global migratory corridors. In this study, using 122,128 Sentinel-1 and 89,752 Sentinel-2 images acquired in 2021 available on the Google Earth Engine platform, we developed a novel two-stage classification for continental-scale wetland mapping and generated the first and up-to-date 10-m resolution wetland map of East Asia. Such a two-stage classification method, which integrates automatic sample generation and spatiotemporal features, combined an initial object-based random forest classifier with a subsequent hierarchical decision tree for secondary waterbody types. The resulting comprehensive map with 3 broad categories and 12 sub-categories in East Asia, named EA_Wetlands, achieved over 88% overall accuracy. According to EA_Wetlands, the total wetland area in this region is 481,802.49 km2, mainly distributed in Northeast China and the Qinghai-Tibet Plateau (41.02%). Of all wetlands in East Asia, about 68.26% are inland wetlands. The highest proportion (29.67%) is identified for inland marsh among 12 sub-categories. Among five countries, China has >88% of the total wetland resources in East Asia, followed by Mongolia (3.57%). South Korea has the largest ratio between wetland and national land areas (10.43%). EA_Wetlands, as the first 10-m resolution wetland data product, will have great applications and benefit wetland conservation and policy management. It will be a critical support for evaluating the implementation of wetland-related international conventions at country and continental scale in East Asia.

Continental-scale wetland mapping: A novel algorithm for detailed wetland types classification based on time series Sentinel-1/2 images

Wetlands have suffered from considerable degradation due to anthropogenic and natural disturbances in recent decades. Although some advancements have been made, effective methods that can produce large-scale wetland maps with detailed categories are still lacking due to the diversity and complexity of wetland ecosystems. To address this issue, we developed a novel algorithm for detailed wetland types classification integrating k-fold random forest and hierarchical decision tree, and so named two-step classification algorithm. Firstly, the phenology-based features were composited based on time series Sentinel-1/2 images, and the k-fold random forest was used to extract five rough wetland types in Google Earth Engine platform. Secondly, the hierarchical decision tree designed based on geometric features was used to separate the rough wetland types into fourteen detailed types. Application of the two-step classification method in Northern, Central and Southern Asia (NCSA) resulted in a continental-scale wetland map with an overall accuracy of 90.0 ± 0.5%. Wetland types, including inland marsh, lake, river, coastal swamp, estuarine water, lagoon, shallow marine water, reservoir, canal/channel and agricultural pond, had good accuracy with both UA and PA over 77%. The remaining wetland types had moderate accuracy, with both UA and PA over 58%. As we calculated, total wetland areas of NCSA were 1,375,489.27 km2. Among the fourteen wetland categories, the inland marsh had the largest area (544,584.38 km2) and was primarily distributed in subarctic and humid continental climates, while the canal/channel had the smallest area (1,651.57 km2) and was primarily scattered in desert, semiarid and humid subtropical climates. The lake and floodplain shared generally large areas with value of 392,413.55 km2 and 173,255.71 km2 respectively, which were typically distributed across desert and semiarid climates. This study successfully mapped continental-scale wetlands with detailed categories at a 10-m spatial resolution, which can provide valuable information for the management of wetland ecosystems and facilitate the implementation of wetland-related sustainable development goals.

Undeveloped and developed phases in the centennial evolution of a barrier-marsh-lagoon system: The case of Long Beach Island, New Jersey

Barrier islands and their associated backbarrier environments protect mainland population centers and infrastructure from storm impacts, support biodiversity, and provide long-term carbon storage, among other ecosystem services. Despite their socio-economic and ecological importance, the response of coupled barrier-marsh-lagoon environments to sea-level rise is poorly understood. Undeveloped barrier-marsh-lagoon systems typically respond to sea-level rise through the process of landward migration, driven by storm overwash and landward mainland marsh expansion. Such response, however, can be affected by human development and engineering activities such as lagoon dredging and shoreline stabilization. To better understand the difference in the response between developed and undeveloped barrier-marsh-lagoon environments to sea-level rise, we perform a local morphologic analysis that describes the evolution of Long Beach Island (LBI), New Jersey, over the last 182 years. We find that between 1840 and 1934 the LBI system experienced landward migration of all five boundaries, including 171 meters of shoreline retreat. Between the 1920s and 1950s, however, there was a significant shift in system behavior that coincided with the onset of groin construction, which was enhanced by beach nourishment and lagoon dredging practices. From 1934 to 2022 the LBI system experienced ~22 meters of shoreline progradation and a rapid decline in marsh platform extent. Additionally, we extend a morphodynamic model to describe the evolution of the system in terms of five geomorphic boundaries: the ocean shoreline and backbarrier-marsh interface, the seaward and landward lagoon-marsh boundaries, and the landward limit of the inland marsh. We couple this numerical modeling effort with the map analysis during the undeveloped phase of LBI evolution, between 1840 and 1934. Despite its simplicity, the modeling framework can describe the average cross-shore evolution of the barrier-marsh-lagoon system during this period without accounting for human landscape modifications, supporting the premise that natural processes were the key drivers of morphological change. Overall, these results suggest that anthropogenic effects have played a major role in the evolution of LBI over the past century by altering overwash fluxes and marsh-lagoon geometry; this is likely the case for other barrier-marsh-lagoon environments around the world.

Sea level rise drives carbon and habitat loss in the U.S. mid-Atlantic coastal zone

Coastal marshes and seagrass beds store millions of tons of carbon in their sediments and sequester carbon at higher per-area rates than most terrestrial ecosystems. There is substantial interest in this “blue carbon” as a carbon mitigation strategy, despite the major threat that sea level rise (SLR) poses to these habitats. Many projections of habitat and carbon change with SLR emphasize the potential for inland marsh migration and increased rates of marsh carbon sequestration, but do not consider carbon fluxes associated with habitat conversion. We integrated existing data and models to develop a spatial model for predicting habitat and carbon changes due to SLR in six mid-Atlantic U.S. states likely to face coastal habitat loss over the next century due to low tidal ranges and sediment supply. Our primary model projection, using an intermediate SLR scenario (1.2 m SLR by 2104), predicts loss of 83% of existing coastal marshes and 26% of existing seagrasses in the study area. In addition, 270,000 hectares of forest and forested wetlands in low-lying coastal areas will convert to coastal marshes. These SLR-driven habitat changes cause the study area to shift from a carbon sink to a source in our primary model projection. Given the many uncertainties about the habitat and carbon changes represented in our model, we also identified the parameters and assumptions that most strongly affected the model results to inform future research needs. These included: land availability for inland marsh migration, the baseline extent and location of coastal marshes, proportion of stored carbon emitted from lost habitats (coastal marsh sediments or terrestrial biomass carbon), and methane emissions from freshwater habitats. The study area switched from a net carbon sink to a net carbon source under SLR for all but three model runs; in those runs, net carbon sequestration declined by 57–99%.

The Crimean-Congo haemorrhagic fever tick vector Hyalomma marginatum in the south of France: Modelling its distribution and determination of factors influencing its establishment in a newly invaded area.

We developed a correlative model at high resolution for predicting the distribution of one of the main vectors of Crimean-Congo haemorrhagic fever virus (CCHFV), Hyalomma marginatum, in a recently colonised area, namely southern France. About 931 H. marginatum adult ticks were sampled on horses from 2016 to 2019 and 2021 in 14 southern French departments, which resulted in the first H. marginatum detection map on a large portion of the national territory. Such updated presence/absence data, as well as the mean number of H. marginatum per examined animal (mean parasitic load) as a proxy of tick abundance, were correlated to multiple parameters describing the climate and habitats characterising each collection site, as well as movements of horses as possible factors influencing tick exposure. In southern France, H. marginatum was likely detected in areas characterised by year-long warm temperatures and low precipitation, especially in summer and mostly concentrated in autumn, as well as moderate annual humidity, compared to other sampled areas. It confirms that even in newly invaded areas this tick remains exclusively Mediterranean and cannot expand outside this climatic range. Regarding the environment, a predominance of open natural habitats, such as sclerophyllous vegetated and sparsely vegetated areas, were also identified as a favourable factor, in opposition to urban or peri-urban and humid habitats, such as continuous urban areas and inland marshes, respectively, which were revealed to be unsuitable. Based on this model, we predicted the areas currently suitable for the establishment of the tick H. marginatum in the South of France, with relatively good accuracy using internal (AUC = 0.66) and external validation methods (AUC = 0.76 and 0.83). Concerning tick abundance, some correlative relationships were similar to the occurrence model, as well as the type of horse movements being highlighted as an important factor explaining mean parasitic load. However, the limitations of estimating and modelling H. marginatum abundance in a correlative model are discussed.

Evaluating relationships between plants, water chemistry, and denitrification potential in palustrine freshwater marshes

Wetlands are hotspots for various biogeochemical processes, including denitrification. Despite its ecological and economic importance, denitrification has proven difficult to measure. Plant community composition, water chemistry, and physical habitat characteristics are all known to play roles in regulating denitrification rates in wetlands. By investigating these relationships, we sought to not only better understand the factors related to denitrification within wetlands, but to build a predictive model capable of estimating the denitrification potential of those wetlands in a more accessible and efficient manner. To accomplish this, we combined a survey of 27 inland marshes throughout Michigan’s Lower Peninsula with a genetic analysis of denitrification potential within their soils. We found that denitrification, and the spatial variation therein, did not vary significantly between vegetation types at either the individual replicate or site levels. Dissolved oxygen and specific conductance were strong predictors of denitrification potential at all levels of analysis, while correlations with plant community composition varied amongst vegetation types. Spatial variation in denitrification potential, both vertically through the soil column and horizontally across the marshes, was most strongly correlated with plant community composition. Lastly, the metric-based predictive model constructed from these relationships was found to be highly predictive of denitrification potential. This model represents both a more accessible method for estimating denitrification in wetlands, and a framework for building similar models in other wetland systems.

Sea-level, storms, and sedimentation – Controls on the architecture of the Andros tidal flats (Great Bahama Bank)

Integrated time-separated remote sensing, geographic information systems (GIS), microbiology, and carbonate sedimentology of the tidal-flat portion of Andros Island (Great Bahama Bank) quantify the geomorphologic change of tidal-flat facies through time and illustrate how sea-level rise, among other controls, is recorded within these deposits. In the 75 years spanned by the remote-sensing datasets, sea level has risen by approximately 10 cm. In the face of this change: a) the seaward margin of the tidal flats has remained static, while b) the internal boundary, which separates the channelized and supratidal marsh deposits, retrograded up to 220 m, broadening the channelized zone, and c) in unison to the broadening of the channelized zone, the abundance of laminated (Scytonema) cyanobacterial fabrics decreased, d) accompanied by the lengthening and avulsion of the network of tidal channels that traverse this zone. Given the amount of sea level rise during the period of observation, such broad-scale restructuring of the Andros tidal-flat architecture is surprising. The muddy tidal flats did not prograde, but instead aggraded and locally retrograded. Whereas retrogradation was minimal at the coastline, the channelized zone retrograded substantially over the more landward supratidal inland marsh. These results question aspects of the autocyclic model for the development of peritidal carbonate cycles, which is underpinned by a dominant behavior of tidal-flat progradation. Whereas other controls cannot be explicitly excluded, sea-level oscillations seemingly exerted considerably more impact on the Andros tidal flat in the last decades than did major storm events.

Patterns of introgression vary within an avian hybrid zone

BackgroundExploring hybrid zone dynamics at different spatial scales allows for better understanding of local factors that influence hybrid zone structure. In this study, we tested hypotheses about drivers of introgression at two spatial scales within the Saltmarsh Sparrow (Ammospiza caudacuta) and Nelson’s Sparrow (A. nelsoni) hybrid zone. Specifically, we evaluated the influence of neutral demographic processes (relative species abundance), natural selection (exogenous environmental factors and genetic incompatibilities), and sexual selection (assortative mating) in this mosaic hybrid zone. By intensively sampling adults (n = 218) and chicks (n = 326) at two geographically proximate locations in the center of the hybrid zone, we determined patterns of introgression on a fine scale across sites of differing habitat. We made broadscale comparisons of patterns from the center with those of prior studies in the southern edge of the hybrid zone.ResultsA panel of fixed SNPs (135) identified from ddRAD sequencing was used to calculate a hybrid index and determine genotypic composition/admixture level of the populations. Another panel of polymorphic SNPs (589) was used to assign paternity and reconstruct mating pairs to test for sexual selection. On a broad-scale, patterns of introgression were not explained by random mating within marshes. We found high rates of back-crossing and similarly low rates of recent-generation (F1/F2) hybrids in the center and south of the zone. Offspring genotypic proportions did not meet those expected from random mating within the parental genotypic distribution. Additionally, we observed half as many F1/F2 hybrid female adults than nestlings, while respective male groups showed no difference, in support of Haldane’s Rule. The observed proportion of interspecific mating was lower than expected when accounting for mate availability, indicating assortative mating was limiting widespread hybridization. On a fine spatial scale, we found variation in the relative influence of neutral and selective forces between inland and coastal habitats, with the smaller, inland marsh influenced primarily by neutral demographic processes, and the expansive, coastal marsh experiencing higher selective pressures in the form of natural (exogenous and endogenous) and sexual selection.ConclusionsMultiple drivers of introgression, including neutral and selective pressures (exogenous, endogenous, and sexual selection), are structuring this hybrid zone, and their relative influence is site and context-dependent.

Vegetation Monitoring in the Saudi Marsh Environment Using Geospatial Technologies

Vegetation in a salt marsh environment differs from that in other environments in terms of species, density, and frequency. In this research, plant communities in some coastal and inland marshes of the Eastern Province of Saudi Arabia were monitored through a quantitative analytical approach using geospatial techniques, namely ArcGIS 10.5 and Erdas Imagine software. Field verification monitored the plant community in each marsh by vision and imaging with regard to the quantitative and qualitative characteristics. Samples were collected in April 2019 and processed by space visuals, extraction of Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) with 30m spatial accuracy, analysis of the mechanical and chemical laboratory, and extraction of X-ray beams for 16 samples. The cartographic analysis of the NG39.SW topographic plate and the MG-A108 geological plate and the analysis of meteorological data for Al-Qaysumah, Al-Dammam, and Al-Ahsa stations for the period 1985–2018 was conducted using SPSS. The results showed that 14 plant species were found in six coastal and inland marsh plant communities. They were distributed in the form of specific areas in which soil salinity plays a prominent role in their distribution. These communities were Zygophyllum Qatarens, Saueda Fruticosa, Halopeplis Perfoliata, Aeluropus Lagopoides, Salsola Imbricate, and Seidlitzia Rosmarinus. The study recommends that grazing, the cultivation of degraded species, and environmental awareness should be regulated.

A comparison of data mining techniques and multi-sensor analysis for inland marshes delineation

Inland Marsh (IM) is a type of wetland characterized by the presence of non-woody plants as grasses, reeds or sedges, with a water surface smaller than 25% of the area. Historically, these areas have been suffering impacts related to pollution by urban, industrial and agrochemical waste, as well as drainage for agriculture. The IM delineation allows to understand the vegetation and hydrodynamic dynamics and also to monitor the degradation caused by human-induced activities. This work aimed to compare four machine learning algorithms (classification and regression tree (CART), artificial neural network (ANN), random forest (RF), and k-nearest neighbors (k-NN)) using active and passive remote sensing data in order to address the following questions: (1) which of the four machine learning methods has the greatest potential for inland marshes delineation? (2) are SAR features more important for inland marshes delineation than optical features? and (3) what are the most accurate classification parameters for inland marshes delineation? To address these questions, we used data from Sentinel 1A and Alos Palsar I (SAR) and Sentinel 2A (optical) sensors, in a geographic object-based image analysis (GEOBIA) approach. In addition, we performed a vectorization of a 1975 Brazilian Army topographic chart (first official document presenting marsh boundaries) in order to quantify the marsh area losses between 1975 and 2018 by comparing it with a Sentinel 2A image. Our results showed that the method with the highest overall accuracy was k-NN, with 98.5%. The accuracies for the RF, ANN, and CART methods were 98.3%, 96.0% and 95.5%, respectively. The four classifiers presented accuracies exceeding 95%, showing that all methods have potential for inland marsh delineation. However, we note that the classification results have a great dependence on the input layers. Regarding the importance of the features, SAR images were more important in RF and ANN models, especially in the HV, HV + VH and VH channels of the Alos Palsar I L-band satellite, while spectral indices from optical images were more important in the marshes delineation with the CART method. In addition, we found that the CART and ANN methods presented the largest variations of the overall accuracy (OA) in relation to the different parameters tested. The multi-sensor approach was critical for the high OA values found in the IM delineation (> 95%). The four machine learning methods can be accurately applied for IM delineation, acting as an important low-cost tool for monitoring and managing these environments, in the face of advances in agriculture, soil degradation and pollution of water resources due to agrochemical dumping.

National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images

Spatially and thematically explicit information of wetlands is important to understanding ecosystem functions and services, as well as for establishment of management policy and implementation. However, accurate wetland mapping is limited due to lacking an operational classification system and an effective classification approach at a large scale. This study was aimed to map wetlands in China by developing a hybrid object-based and hierarchical classification approach (HOHC) and a new wetland classification system for remote sensing. Application of the hybrid approach and the wetland classification system to Landsat 8 Operational Land Imager data resulted in a wetland map of China with an overall classification accuracy of 95.1%. This national scale wetland map, so named CAS_Wetlands, reveals that China’s wetland area is estimated to be 451,084 ± 2014 km2, of which 70.5% is accounted by inland wetlands. Of the 14 sub-categories, inland marsh has the largest area (152,429 ± 373 km2), while coastal swamp has the smallest coverage (259 ± 15 km2). Geospatial variations in wetland areas at multiple scales indicate that China’s wetlands mostly present in Tibet, Qinghai, Inner Mongolia, Heilongjiang, and Xinjiang Provinces. This new map provides a new baseline data to establish multi-temporal and continuous datasets for China’s wetlands and biodiversity conservation.

Species‐Habitat Relationships and Priority Areas for Marsh‐Breeding Birds in Ontario

ABSTRACTPopulations of marsh‐breeding birds have declined throughout the southern Laurentian Great Lakes basin. To advance conservation of these species, we used occupancy modeling, a regional prioritization scheme, and data from Birds Canada's Great Lakes Marsh Monitoring Program (2016–2018) to describe species‐habitat relationships and identify priority habitat areas for 7 obligate marsh‐breeding bird species in southern Ontario, Canada: American bittern (Botaurus lentiginosus), common gallinule (Gallinula galeata), least bittern (Ixobrychus exilis), marsh wren (Cistothorus palustris), pied‐billed grebe (Podilymbus podiceps), sora (Porzana carolina), and Virginia rail (Rallus limicola). Given these species respond to land cover at widely varying spatial scales, we initially identified the most informative scale (buffer = 100 m, 200 m, 400 m, 800 m, 1,600 m, 3,200 m, or 6,400 m) for marsh, urban, agricultural, and forest cover to increase model performance. We also considered climate variables, whether sample sites were along a Great Lakes coastline or inland, and covariates influencing detection. Occupancy was best explained by land cover at a wide range of spatial scales depending on the species. All species except Virginia rail responded positively to marsh cover; American bittern and Virginia rail responded negatively to urban cover; least bittern, pied‐billed grebe, and Virginia rail responded negatively and sora responded positively to agricultural cover; and American bittern, common gallinule, marsh wren, and pied‐billed grebe responded negatively and Virginia rail responded positively to forest cover. Only American bittern responded negatively to mean May–June temperature; only pied‐billed grebe responded positively to start of growing season; and only Virginia rail had higher occupancy at inland marshes compared to coastal. We combined predictions from the best model for each of 5 species with reasonably good model fit (we excluded sora and Virginia rail) to identify priority habitat areas for marsh‐breeding birds. Expansion of wetland conservation work from existing priority areas based on waterfowl to also include these new additional priority areas based on marsh‐breeding birds will be an important step towards conservation of all birds, and will help slow or maybe even reverse declining population trends. Some restoration activities outside but adjacent to priority areas will also be important for rebuilding marshes for these species across this intensively farmed and developed region. © 2020 The Wildlife Society.

Connection between prey composition and the landscape structure in the hunting area of Barn Owl’s (Tyto alba) in Baranja (Croatia)

Background and purpose: The assumption that the species composition and the relative abundance of small mammals in pellets of Barn Owls reflects the landscape structure of the hunting area is tested, based on habitat preferences of small mammals identified from pellets collected in the hilly and lowland parts of Baranja county (Croatia). Materials and methods: During 2007 we collected 2395 whole pellets and their fragments in 21 localities, from which 6613 prey remains were identified as belonging to small mammals (99.5%) of 23 species. The correlation between the relative abundance of mammal species and landscape structures (habitat types and landscape features) was tested. Results: There was a significant correlation between the relative abundance of seven small mammal species and the proportion of particular landscape structure classes. The number of small mammal species showed a negative correlation with the area of inland marshes. The evenness of the small mammal fauna grew with the mosaicity of landscape and the length of the borders in the owl’s hunting area. In the total prey the Common Vole (Microtus arvalis) dominated with more than 62%, which indicates its population outbreak. The diversity and evenness of small mammals in the hilly and lowland regions did not differ. Conclusions: We found significant correlations between the relative abundance of some small mammal species and the landscape structure classes in the owls’ hunting area. Our results suggested that the diversity of small mammals increases as the mosaic of the landscape increases, while the degree of population outbreak of the Common Vole decreases. These relationships should be taken into consideration when designing landscapes or changing land use.

A Tool for Rapid Assessment of Hydrological Connectivity Patterns in Texas Coastal Wetlands: Linkages between Tidal Creeks and Coastal Ponds

Coastal salt marshes are heterogeneous, spatially complex ecosystems. The degree of hydrological connectivity in these systems can be a significant driver in the flux of energy, organisms, and nutrients across the marsh landscape. In tidally driven systems, the frequency and magnitude of hydrological connection events results in the creation of a matrix of intermittently connected coastal wetland habitats, some of which may be hydrologically isolated or partially drained at any given time. Previous approaches to understanding landscape-level hydrologic connectivity patterns have required either intensive long-term monitoring or spatially explicit modeling. In this paper, we first describe a 13-month field study in the Guadalupe Estuary of the Texas Gulf Coast that linked hydrological connectivity patterns between a saltwater pond to water levels in an adjacent tidal creek and nearby San Antonio Bay. We next describe the integration of these field data with high-resolution digital elevation models and environmental parameters to develop a spatially explicit model that is a Simulation of Landscape-level Oscillations in Salt Marsh Hydroperiod (SLOSH). We evaluated the ability of SLOSH to simulate trends in landscape-level patterns of hydrological connectivity between a tidal creek and an inland marsh pond. Magnitude and periodicity of simulated and observed water-level fluctuations in the pond were similar. Highest creek water levels, resulting in high frequency and duration of hydrological connectivity with the pond, corresponded with the highest bay water levels, which occurred during September and October. Lowest creek water levels, resulting in low frequency and duration of hydrological connectivity, corresponded with the lowest bay water levels, which occurred during December through February. By simulating the pulsing structure of salt marsh hydrology, SLOSH creates the foundation on which to assess how additional drivers (precipitation, wind, freshwater inflows, etc.) can influence coastal marsh hydrology and overall ecology. Citation: Swannack TM, Wozniak JR, Grant WE, Davis SE III. 2019. A tool for rapid assessment of hydrological connectivity patterns in Texas coastal wetlands: linkages between tidal creeks and coastal ponds. Texas Water Journal. 10(1):46-59. Available from: https://doi.org/10.21423/twj.v10i1.7073.

Coastal Wetland Geomorphic and Vegetative Change: Effects of Sea-Level Rise and Water Management on Brackish Marshes

Conservation and management of coastal ecosystems require an understanding of how accelerated sea-level rise (SLR) and altered hydrology impact community shifts over time. This study evaluates the response of tidal wetlands of the Ten Thousand Islands, Collier County, Florida, to SLR and water management, with a focus on the development and distribution of tidal ponds across a wetland landscape. Sediment cores collected from marshes, mangroves, and tidal ponds reveal a clear transgressive stratigraphy. Facies analyses demonstrate that ponds originate from the surface downward through the degradation of marsh peat. Analyses of 1953 and 2009 aerial imagery using ArcGIS® software clearly identified tidal pond initiation, growth, and merger over time. Wetlands west of the Faka Union Canal, which have limited freshwater sheet flow due to canalization, are experiencing a greater increase in pond count, pond density (p = 0.0038), and mean pond area (p < 0.0001). Qualitative observations also recognize a relatively larger influence of mangrove envelopment over time in western sites compared with those retaining near-natural flows. Future land management plans must account for the expected submergence of inland marsh ecosystems driven by SLR and accelerated by hydrologic alteration. Continued restoration of freshwater sheet flow is necessary for slowing the regional transition (and loss) of graminoid marshes to either mangrove or pond environments. Without such action, a complete loss of these biologically diverse marsh ecosystems as mangrove forests encroach and marsh surfaces submerge is probable in the short term.

The impact of past management practices on tidal marsh resilience to sea level rise in the Delaware Estuary

Defining appropriate management and conservation strategies to maximize tidal marsh resilience to sea level rise requires a clear understanding of the causes of marsh degradation. While sea level rise is a well-known threat to tidal marshes, current and past management practices on marshes can also greatly influence present-day marsh condition, resilience and future persistence. Using point-intercept analysis of maps and imagery, we assessed the past and current landcover and elevation of Delaware Estuary tidal marshes in New Jersey, USA. We estimated the historic extent of tidal marsh impoundment for agriculture and determined current marsh vegetation composition and elevation in areas that were and were not historically impounded. We estimate that more than half of all tidal marsh in the 36,539 ha study area had been historically impounded. A small fraction of this area remains impounded at present (7.6%). While tidal flow has since returned to formerly diked areas, marsh recovery has been incomplete. Overall 21.6% (4048.8 ha) of formerly impounded marsh has not revegetated, becoming open water after impoundment breaches. Marsh loss as a result of impoundment is also responsible for the loss of 2.3 km of adjacent shoreline beaches. Conversely, only 0.5% of marsh that was never impounded has converted to open water since 1931. This difference is likely due to dramatic elevation deficits caused by impoundment. Marsh elevation of current and formerly impounded areas (derived from LiDAR and validated with RTK GPS) is significantly lower than the elevation of marsh areas that were never impounded. Supporting this finding, the frequency of high marsh vegetation (an indicator of higher elevation)in vegetated formerly impounded areas is half that of areas that were never impounded. Marsh edge erosion and creek expansion have added an additional estimated 3836 ha to the amount of tidal marsh loss since 1931. Marsh transgression inland into forest and agricultural areas has resulted in estimated gains in marsh area of 2815 ha, offsetting a considerable proportion of losses. Given our results, we recommend the following management actions to maximize tidal marsh persistence in the Delaware Estuary: (1) Beneficial use of sediment to offset marsh elevation deficits resulting from historic impoundment, (2) Strategic land protection to maximize the potential for inland marsh migration, (3) Tidal flow restoration to remaining impounded areas in combination with the beneficial use of sediment to address elevation deficits. Determining the impacts to tidal marshes from past management practices makes it possible parse the relative contribution of relative sea level rise and site-level management, resulting in more targeted conservation strategies.

Chasing boundaries and cascade effects in a coupled barrier-marsh-lagoon system

The long-term dynamic evolution of an idealized barrier-marsh-lagoon system experiencing sea-level rise is studied by coupling two existing numerical models. The barrier model accounts for the interaction between shoreface dynamics and overwash flux, which allows the occurrence of barrier drowning. The marsh-lagoon model includes both a backbarrier marsh and an interior marsh, and accounts for the modification of the wave regime associated with changes in lagoon width and depth. Overwash, the key process that connects the barrier shoreface with the marsh-lagoon ecosystems, is formulated to account for the role of the backbarrier marsh. Model results show that a number of factors that are not typically associated with the dynamics of coastal barriers can enhance the rate of overwash-driven landward migration by increasing backbarrier accommodation space. For instance, lagoon deepening could be triggered by marsh edge retreat and consequent export of fine sediment via tidal dispersion, as well as by an expansion of inland marshes and consequent increase in accommodation space to be filled in with sediment. A deeper lagoon results in a larger fraction of sediment overwash being subaqueous, which coupled with a slow shoreface response sending sediment onshore can trigger barrier drowning. We therefore conclude that the supply of fine sediments to the back-barrier and the dynamics of both the interior and backbarrier marsh can be essential for maintaining the barrier system under elevated rates of sea-level rise. Our results highlight the importance of considering barriers and their associated backbarriers as part of an integrated system in which sediment is exchanged.