Permanent Wetlands Research Articles

Habitat use by nomadic ibis and spoonbills post-dispersal from breeding sites

Abstract Context Nomadic waterbirds are highly mobile across a range of spatial and temporal scales, which makes it difficult to monitor, quantify, and predict their habitat use with traditional methods, especially between breeding events when individuals and flocks can move over vast areas. Objectives This study aimed to provide accurate information on habitat use to improve strategic conservation management of these species, particularly the provisioning of environmental water. Methods To overcome the challenges of distance and remoteness, we analysed a 7-year GPS satellite telemetry dataset from 141 individuals. We quantified habitat selection post-dispersal from breeding sites, and predicted habitat preference for two wading waterbird species of the Threskiornithidae family that frequently nest together at the same sites: straw-necked ibis (Threskiornis spinicollis) and royal spoonbill (Platalea regia). Results Both long-term and short-term landscape-scale habitat associations differed between species. Royal spoonbills used fewer and more restricted habitat types than straw-necked ibis. Spoonbills displayed strong preferences for reservoirs, marshes and permanent wetlands, while ibis used both aquatic and terrestrial habitat, including areas of intensive animal production, modified pasture, and woodlands. Analysis of nocturnal versus diurnal space use showed that roosting and foraging habitat requirements for both species are distinct. Conclusions Analysing over 1 million telemetry points revealed species-level variability in habitat use, informing resource allocation for environmental water management. Royal spoonbills are more vulnerable to habitat change due to water regime alterations, highlighting the need for focused conservation management. Differences in day and night habitat use indicate the necessity of considering roosting habitats alongside foraging habitats for effective conservation. This comprehensive understanding of waterbirds' spatiotemporal interactions with their environment is crucial for long-term management aimed at increasing waterbird numbers and maintaining diversity.

Effects of Urbanization on Community Composition and Conditions of Birds in Wetland Ecosystems

Urbanization in wetlands has negatively impacted bird species diversity and populations. It is important to be able to predict the consequences for avian biodiversity and to identify wetlands of special value to the maintenance of local wetland communities. This study involved conducting point counts across four permanent urban wetlands at the University of Florida and three retention ponds in the Gainesville, FL vicinity to observe patterns in wetland bird species diversity and populations. This study also utilized data from the Florida Museum of Natural History (FLMNH) to explore differences in phenotypes of wetland bird specimens. The local wetlands showed trends where specific species thrived in one type of area versus the other. Egrets were frequently observed in larger wetlands, and double-crested cormorants were observed more frequently in smaller ones. Additionally, retention ponds showed promise as a conservation method, dependent on their depth and size. The largest retention pond attracted many bird species in great numbers, showing promise for methods to negate negative the impact of human activity and climate change. Characterization for data collected at the FLMNH on both weight and species analyzed across numerous regional locations showed a negative trend between specimen weight and time progression.

Mobilizing Evidence-based Knowledge for Sustainable Wetlands Co-management and Co-governance amidst increasing Anthropogenic and Environmental Stressors: Key Lessons from Mityana District, Uganda

Wetlands (covering about 1.5–1.6 billion hectares globally), are critical biodiversity and livelihood hotspots. Wetlands further replenish the global economy with $47.4 trillion/year worth of ecosystem services. By jealously guarding wetlands, progress toward sustainable development goals, and livelihood welfare are possible. Unfortunately, despite the promulgation of wetland governance mechanisms, 35 percent of the global natural wetlands have been lost since the 1970s. This could be worse in undocumented or explored wetland zones situated in remote tropical regions. In this study, we bring to the fore insights from 286 documents sourced from Scopus and engagements from 105 citizens in Mityana, to (i) map wetlands (including the current vulnerabilities and threats), and (ii) co-develop a wetlands management action pathway that could create sustainable co-management possibilities and sustainable livelihood futures. Findings revealed that although research on wetlands has increased for the last 31 years, since 2021, it has plummeted. In Uganda, wetland research and scholarship is predominantly situated around the Lake Victoria region. Most research focuses on natural or biological sciences. Emerging policy themes and trending research topics are shifting from key wetland management paradigms. From a total of 105 sampled wetlands scattered across fourteen (14) sub-counties in the Mityana district, critical wetland issues were unraveled. Mityana is crossed by two wetland systems (Lake Wamala and River Mayanja dominated by permanent papyrus and seasonal swamps respectively. Wetlands offer unique livelihood, cultural assets/capitals, and ecological benefits (including cultural/aesthetics meaning). An unfathomable rate of degradation is evident. Anthropogenic factors are the predominant threat drivers, especially eucalyptus planting. The loss of culturally valuable wetlands has increased socio-cultural-ecological grief, such as around Lake Wamala. Micro-level management actions are increasing, albeit mainly around accessible permanent wetlands. Most riparian wetland sedentary populations expressed willingness and interest in the co-management and governance of community wetlands. More robust actions and pathways are needed to create avenues for community co-management. The co-developed the sustainable wetlands management action pathway (SWeMAP) provides seven (7) coherent steps, including critical social science insights that could aid sustainable wetlands governance and management across geographies. As wetlands in Uganda have been gazetted as critical to sustainable development, the urgent co-development and financing of micro-level wetland action plans, including situational inventories could help create avenues for sustainable wetlands management.

High-precision estimation of pan-Arctic soil surface temperature from MODIS LST by incorporating multiple environment factors and monthly-based modeling

Global warming has shown an “Arctic amplification effect” in recent decades, leading to pronounced changes in pan-Arctic soil surface temperature (SST). SST plays a direct role in energy exchange between soil and atmosphere and serves as an indicator of the land–atmosphere energy balance. Remote sensing land surface temperature (LST) data is able to indicate near-surface temperature, but influences from environment factors, such as vegetation and snow, can introduce biases between LST and SST. In this study, the importances of five environment factors (vegetation, snow, surface soil composition, topography, and solar radiation) to monthly mean SST estimation from MODIS LST in pan-Arctic were analyzed. Then a method for pan-Arctic monthly mean SST estimation from MODIS LST by incorporating these environment factors and monthly-based modeling based on random forest (RF) algorithm was proposed. The results reveal that all the selected environment factors contribute to monthly-based modeling, with vegetation exerting the greatest importance from May to October and snow in March and April. The root mean square error (RMSE) of pan-Arctic monthly SST estimated by the proposed method from 2003 to 2022 ranges from 0.89 to 1.88 °C, which is a 42.95–––53.35 % reduction compared to the widely used season-based multivariate linear regression (MLR) models based solely on LST (RMSE between 1.56 and 4.03 °C). The accuracy is notably improved in areas with lower and no vegetation (grassy woodlands, grasslands, permanent wetlands, and barrens) in the cold season (September to the following April), and in higher vegetation (forests) areas in the warm season (May to August). The proposed method can contribute to producing high-precision monthly mean SST data from LST, estimating permafrost extent and active layer thickness, and understanding the land–atmosphere energy balance in pan-Arctic.

Evaluation of GPM IMERG Early, Late, and Final Run in Representing Extreme Rainfall Indices in Southwestern Iran

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

Assessing changes in the ecosystem service value in response to land use and land cover dynamics in Malawi.

Land use and land cover (LULC) changes are inevitable outcomes of socioeconomic changes and greatly affect ecosystem services. Our study addresses the critical gap in the existing literature by providing the first comprehensive national analysis of LULC changes and their impacts on ecosystem service values (ESVs) in Malawi. We assessed changes in ecosystem service values (ESVs) in response to LULC changes using the benefit transfer method in ArcGIS 10.6 software. Our findings revealed a significant increase in grasslands, croplands, and urban areas and a notable decline in forests, shrubs, wetlands, and water bodies. Grassland, cropland, and built-up areas expanded by 52%, 1%, and 23.2%, respectively. In contrast, permanent wetlands, barren land, and water bodies declined by 27.6%, 34.3%, and 1%, respectively. The ESV declined from US$90.87 billion in 2001 to US$85.60 billion in 2022, marking a 5.8% reduction. Provisioning services increased by 0.5% while regulating, supporting, and cultural ecosystem service functions declined by 12.2%, 3.16%, and 3.22%, respectively. The increase in provisioning services was due to the expansion of cropland. However, the loss of regulating, supporting, and cultural services was mainly due to the loss of natural ecosystems. Thus, environmental policy should prioritise the conservation and restoration of natural ecosystems to enhance the ESV of Malawi.

Analysis of the temporal and spatial evolution of turbidity in Tonle Sap Lake and its influencing factors

Turbidity is a crucial indicator of water quality. The European Commission's Copernicus Land Monitoring Service Platform provides free turbidity data for large lakes to monitor the global water quality of lakes. However, the data were missing from April 2012 to April 2016, severely limiting long-term analysis. Based on MODIS and turbidity data, Random Forest and XGBoost models are used to invert Tonle Sap Lake's turbidity. Random Forest outperformed the XGBoost model. Based on Random Forest model, missing data were filled in to construct long-term series data of Tonle Sap Lake turbidity (2004–2021). Trend, persistence and correlation analyses were conducted to reveal spatiotemporal characteristics and driving mechanism of turbidity. The results showed that: (1) spatially, the average annual, monthly, and seasonal turbidity was higher in the north but lower in the south, with regions of higher turbidity exhibiting more significant changes; (2) temporally, the annual turbidity mean was 53.99 NTU and showed an increasing trend. Monthly, turbidity values were higher from March to August and lower from September to February, with the highest and lowest recorded in June and November at 110.06 and 5.82 NTU, respectively. Seasonally, turbidity was higher in spring and summer compared to autumn and winter, with mean turbidity values of 84.16, 93.47, 15.33 and 23.21 NTU, respectively; (3) In terms of sustainability, the Hurst exponent for annual turbidity was 0.23, indicating a reverse trend in the near future; (4) Dam construction's impact on turbidity was not significant. Compared with natural factors (permanent wetlands, grasslands, lake surface water temperature, and remote sensing ecological index), human activities (barren, urban and built-up lands, croplands and population density) had a more significant impact on turbidity. Turbidity was highly correlated with croplands (r = 0.76), followed by population density (r = 0.71), and urban and built-up lands (r = 0.69).

Combining multisensor images and social network data to assess the area flooded by a hurricane event.

In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km2), followed by deciduous forest (1,591 km2) and sub-perennial forest (1,317 km2). About 1,608 km2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km2, with the agricultural class as the most impacted (735 km2). Our analysis detected the saltmarsh class occupied 541 km2in the LULC map, and around 328 km2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.

Navigating through Complexity by Profiling the Main Threats to Sustainable Tropical Wetlands Management and Governance: A Case Study of Mityana District, Uganda

Wetlands are crucial ecosystems that promote sustainable livelihood and biodiversity conservation, especially in tropical regions. However, recent studies have reported increasing threats to wetlands both environmental and human which threaten the provision and acquisition of several wetland benefits. Though there is recognition of the value of wetlands, in most local communities in Uganda; especially around the Lake Wamala region in Mityana district, there is still limited knowledge and research on the benefits of wetlands and the level of threat thus affecting wetland governance. This research addresses this gap by using participatory research to create a baseline inventory that could be used by wetland managers to identify the main wetlands threats to inform policy on how to develop participatory actions and local area-based management practices. The study was conducted in 14 sub-counties and a sample of 105 wetlands along the wetlands of Lake Wamala and River Mayanja wetlands system in Mityana District was captured. Study findings revealed increased human threats to wetlands especially in the River Mayanja seasonal wetlands zone. The increased effects of environmental threats such as climate change are also altering permanent wetlands along Lake Wamala. Our developed inventory based on local participants' perspectives revealed that since the level of damage to wetlands in Mityana especially the permanent wetlands is relatively low, a focus on developing new mapping, and a decentralized approach to wetland management that focuses on capacity building, development of wetlands action plans, increased support to local wetland authorities, and a system thinking approach in wetland threat identification and management could help in the regeneration of most wetlands zones.

Uncovering extensive populations of the ‘threatened’ Black Crowned Cranes in Gambella, Ethiopia

A reliable assessment of the population size of threatened species is crucial for making informed decisions about their conservation and management. However, there is a lack of comprehensive knowledge on the population of the eastern Black Crowned Crane (Balearica pavonina ceciliae), which is globally recognized as a threatened taxon experiencing a declining population trend. Here, we investigated the density, abundance and factors that influence flock size in the wetlands of the Gambella region in Ethiopia. We used line transects to collect data during dry and wet seasons. Population density and abundance were estimated using DISTANCE software, whereas factors influencing crane flock size were evaluated using a generalized linear model. We found that crane density ranged from 444 to 965 birds per sq km in the dry season and from 62 to 105 individuals per sq km in the wet season. We estimated that the population size within the designated survey areas as 7634 during the dry season and 866 during the wet season. Furthermore, for the broader survey region that encompasses permanent wetlands, the estimated population ranged from 9138 to 19,849 individuals in the dry season and from 1272 to 2166 individuals in the wet season. We found that grazing intensity and proximity to woodlands and rivers had significant positive impacts on crane flock size, whereas an increase in wetland water depth and grass height remarkably reduced flock size. As a result, wetland habitat degradation-related problems are not currently a threat to cranes in this region, but are likely to increase in the future due to large-scale agricultural expansion. The crane population size we estimated in Gambella is high, three to four times higher than the estimate from the entirety of Ethiopia. Our research underscores the global significance of Gambella wetlands as an important habitat for cranes. However, to obtain an accurate population estimate in this region, it is imperative to include seasonal wetlands, savannah grasslands, farmlands and the extended wetlands of South Sudan, which are hydrologically connected to the wetlands of Gambella region. To achieve this, comprehensive surveys and estimations should incorporate various techniques, such as aerial surveys, satellite telemetry, and high-resolution satellite imagery.

Impacts of climate and land coverage changes on potential evapotranspiration and its sensitivity on drought phenomena over South Asia

AbstractUnderstanding the spatiotemporal historical drought pattern and their sensitivity effect on potential evapotranspiration (PET) and vegetation coverage changes is essential for efficient drought mitigation policies under climate change. In this study, we used the standardized precipitation evapotranspiration index (SPEI) at multiple timescales, such as SPEI‐01, SPEI‐03, SPEI‐06, SPEI‐09 and SPEI‐12; we explored their regional‐scale dry and wet annual changes across seven sub‐regions of South Asia from 1902 to 2018. Results suggest that from 1981 to 2018, the extreme drought of SPEI has increased in South Asia, which mostly affects the summer and winter growing seasons, that is, SPEI‐06 to SPEI‐12 across seven sub‐regions of South Asia. The frequency of drought events during dry and wet annual changes of SPEI had an extremely dry year starting from 1998 to 2018, which mostly affected the South Asia region. Data from the past 18 years showed that the land changing detection has increased in the forests, cultivated land, arid land, savanna and farmland; by contrast, there has been significantly reduced permanent ice and snow, mixed forests, open shrub, grasslands, permanent wetlands, water bodies and evergreen broadleaf forests. Seasonal SPEI presented diverse characteristics such as showing a dry trend in Afghanistan, India, Pakistan and Sri Lanka during autumn and winter. Afghanistan and Bhutan are wet during the summer compared with other sub‐regions of South Asia, with drought frequency occurring at 45.3% and 44.4%. Sri Lanka, Pakistan and India are the driest regions in South Asia due to their high drought frequency, duration and intensity. The correlation between PET and crop water stress index (CWSI), PET and regional ETp reduction (Er) indicated a considerably negative correlation, while a significantly positive correlation was found between CWSI and Er, NDVI and Er. This study provides a comprehensive assessment of climate and vegetation coverage changes on PET, ET and SPEI, and can help in formulating long‐term adaptive strategies to reduce the cumulative impacts of droughts.

Role of vernal pools in activity and diversity of bats and birds linked with the emergence of flying insects

Vernal pools are small temporary wetlands occurring in shallow depressions usually filled with precipitation in autumn and winter. They will dry seasonally, which makes them fishless ponds devoid of large aquatic predators. These ephemeral ponds are less studied compared to other freshwater ecosystems, such as lakes, although they are considered biodiversity hotspots. The aim was to study the use of vernal pools by birds and bats and compare this to permanent wetlands. We expected higher bat activity and bird abundance in vernal pools than permanent wetlands, and that is related to the spatial and temporal fluctuations of the emerging flying insects. However, no difference in bird abundance, diversity or community was found between the habitats. Regarding bats, the activity of Myotis spp was generally higher at lakes than vernal pools, while the activity of Eptesicus nilssonii increased during the study period regardless of habitat type. No relationship was found between the emergence of insects and the occurrence of bird and bats. Although, vernal pools were not more attractive habitats compared to lakes, they still form an important, largely neglected, diverse habitat type within the boreal landscape.

Analysis of the spatiotemporal changes in global land cover from 2001 to 2020

Land cover changes have far-reaching effects on climate change and the evolution of ecological environments, making them important components of global change research. Based on MODIS land cover data (MCD12Q1), statistical parameters such as land cover dynamic degree, transition probability, and transition matrix were employed to analyse the spatiotemporal characteristics of global land cover changes from 2001 to 2020. The results indicated that land cover exhibits a latitudinal distribution in mid-latitude regions and a longitudinal distribution in high and low-latitude regions. During the study period, the areas of evergreen needleleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, closed shrublands, and permanent wetlands exhibited fluctuating changes. Evergreen broadleaf forests and barren lands have decreased annually, whereas mixed forests, woody savannas, grasslands, urban and built-up areas, and cropland/natural vegetation mosaics have increased annually. In terms of the dynamic degree, the most significant changes occurred in deciduous broadleaf forests, closed shrublands, permanent wetlands, and cropland/natural vegetation mosaics. Additionally, there were significant variations in the types and regions of land cover changes and transitions. Evergreen needleleaf forests transitioned into grassland types, and the conversion between grassland types was the main transition from mid- to high- latitude regions. In tropical areas, evergreen broadleaf forests, open shrublands, and croplands have shifted towards grassland types as primary transitions. These findings provide significant implications for the in-depth exploration of land-atmosphere interactions, climate change, and numerical models.

Spatio-Temporal Change of Land Use/Land Cover and Vegetation Using Multi-MODIS Satellite Data, Western Ethiopia.

Land use and land cover (LULC) change and variability are some of the challenges to present-day water resource management. The purpose of this study was to determine LULC and Normalized Difference Vegetation Index (NDVI) fluctuations in western Ethiopia during the last 20 years. The first part of the study used MODIS LULC data for the change analysis, change detection, and spatial and temporal coverage in the study region. In the second part, the study analyzes the NDVI change and its spatial and temporal coverage. In this study, The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were applied to determine LULC and NDVI changes over four different periods. Evergreen broadleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, grasslands, permanent wetlands, croplands, urban and built-up lands, and water bodies are the LULC in the period of analysis. The overall classification accuracy for the classified image from 2001 to 2020 was 85.4% and the overall kappa statistic was 81.2%. The results indicate a substantial increase in woody savannas, deciduous broadleaf, grasslands, permanent wetlands, and mixed forest areas by 119.6%, 57.7% 45.2%, 37%, and 21.3%, respectively, followed by reductions in croplands, water bodies, savannas, and evergreen broadleaf forest by 90.1%, 19.8%, 13.2%, and 4.8%, respectively, for the catchment between 2001 and 2020. The result also showed that the area's vegetation cover increased by 64% from 2001 to 2022. This study could provide valuable information for water resource and environmental management as well as policy and decision-making.

The Susceptibility of Wetland Areas in the Yangtze River Basin to Temperature and Vegetation Changes

Wetlands serve a critical function in water storage and ecological diversity maintenance. However, human activities have resulted in wetland loss in the middle and lower reaches of the Yangtze River Basin (MLYRB), while the wetland distribution in this area shows great discrepancy in previous estimates. It is, therefore, imperative to estimate the distribution of potential wetlands at present and project their variation under future climate change scenarios. In this study, we simulate the wetland distribution in the MLYRB at 15″ resolution using 5 machine learning methods with 19 predicting factors of topographic index, vegetation index, climate data, hydrological data, and soil type data. A 5-fold cross-validation with observed permanent wetlands shows that the reconstructions from Adaptive Boosting tree (AdaBoost) algorithm have the highest accuracy of 97.5%. The potential wetland area in the MLYRB is approximately ~1.25 × 105 km2, accounting for 15.66% of the study region. Direct human activities have led to the loss of nearly half of the potential wetlands. Furthermore, sensitivity experiments with the well-trained models are performed to quantify the response of the total wetland area to each influencing factor. Results indicate vulnerability of wetland areas to increases in leaf area index (LAI), coldest season temperature, warmest season temperature, and solar radiation. By the 2100s, the potential wetland area is expected to decrease by 40.5% and 50.6% under the intermediate and very high emissions scenarios, respectively. The changes in LAI and the coldest season temperature will contribute to 50% and 40% of this loss of potential wetlands, respectively. Wetland loss may further undermine biodiversity, such as waterfowl, and fail to provide functions such as flood protection, and water supply. This work reveals the spatial pattern of potential wetland areas and their sensitivity to climate changes, stressing the need for effective strategies to mitigate wetland loss at specific regions in the MLYRB.

Predicting Inundation Dynamics and Hydroperiods of Small, Isolated Wetlands Using a Machine Learning Approach

The duration of inundation or saturation (i.e., hydroperiod) controls many wetland functions. In particular, it is a key determinant of whether a wetland will provide suitable breeding habitat for amphibians and other taxa that often have specific hydrologic requirements. Yet, scientists and land managers often are challenged by a lack of sufficient monitoring data to enable the understanding of the wetting and drying dynamics of small depressional wetlands. In this study, we present and evaluate an approach to predict daily inundation dynamics using a large wetland water-level dataset and a random forest algorithm. We relied on predictor variables that described characteristics of basin morphology of each wetland and atmospheric water budget estimates over various antecedent periods. These predictor variables were derived from datasets available over the conterminous United States making this approach potentially extendable to other locations. Model performance was evaluated using two metrics, median hydroperiod and the proportion of correctly classified days. We found that models performed well overall with a median balanced accuracy of 83% on validation data. Median hydroperiod was predicted most accurately for wetlands that were infrequently inundated and least accurate for permanent wetlands. The proportion of inundated days was predicted most accurately in permanent wetlands (99%) followed by frequently inundated wetlands (98%) and infrequently inundated wetlands (93%). This modeling approach provided accurate estimates of inundation and could be useful in other depressional wetlands where the primary water flux occurs with the atmosphere and basin morphology is a critical control on wetland inundation and hydroperiods.

A wetland permanence classification tool to support prairie wetland conservation and policy implementation

AbstractWetland permanence, the duration and frequency that surface water is present, affects biological communities and whether wetlands are protected under legislation in some jurisdictions. Wetland drainage in the Prairie Pothole Region (PPR) has changed the distribution of wetlands because smaller and more temporary wetlands are more likely to be drained. This change in distribution affects biodiversity and other wetland ecosystem services. In Manitoba, Canada, wetlands are treated differently under the Water Rights Act based on permanence classification and can either be drained with a simplified registration (temporary and ephemeral wetlands), drained with a permit requiring mitigation (seasonal wetlands), or are protected from drainage (semipermanent and permanent wetlands). To facilitate implementing a conservation program targeting the most vulnerable wetlands, we built a classification model using LiDAR and Sentinel‐2 data (1312 training observations). Our random forest model had 73% accuracy on 563 test observations and is applicable across the agricultural region of southwestern Manitoba. We predicted the wetland permanence class of 365,499 wetlands and built an online tool to help practitioners implement a conservation program that pays producers to conserve temporary and ephemeral wetlands. Our approach is applicable elsewhere in the PPR and other regions with variation in wetland permanence.

Current availability and distribution of Congo Basin’s freshwater resources

The Congo Basin is of global significance for biodiversity and the water and carbon cycles. However, its freshwater availability and distribution remain relatively unknown. Using satellite data, here we show that currently the Congo Basin’s Total Drainable Water Storage lies within a range of 476 km3 to 502 km3, unevenly distributed throughout the region, with 63% being stored in the southernmost sub-basins, Kasaï (220–228 km3) and Lualaba (109–169 km3), while the northern sub-basins contribute only 173 ± 8 km3. We further estimate the hydraulic time constant for draining its entire water storage to be 4.3 ± 0.1 months, but, regionally, permanent wetlands and large lakes act as resistors resulting in greater time constants of up to 105 ± 3 months. Our estimate provides a robust basis to address the challenges of water demand for 120 million inhabitants, a population expected to double in a few decades.

Data integration reveals dynamic and systematic patterns of breeding habitat use by a threatened shorebird

Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000–2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.

Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000–2018

Recent rapid warming has caused uneven impacts on the composition, structure, and functioning of northern ecosystems. It remains unknown how climatic drivers control linear and non-linear trends in ecosystem productivity. Based on a plant phenology index (PPI) product at a spatial resolution of 0.05° over 2000–2018, we used an automated polynomial fitting scheme to detect and characterize trend types (i.e., polynomial trends and no-trends) in the yearly-integrated PPI (PPIINT) for northern (> 30°N) ecosystems and their dependence on climatic drivers and ecosystem types. The averaged slope for the linear trends (p < 0.05) of PPIINT was positive across all the ecosystems, among which deciduous broadleaved forests and evergreen needle-leaved forests (ENF) showed the highest and lowest mean slopes, respectively. More than 50% of the pixels in ENF, arctic and boreal shrublands, and permanent wetlands (PW) had linear trends. A large fraction of PW also showed quadratic and cubic trends. These trend patterns agreed well with estimates of global vegetation productivity based on solar-induced chlorophyll fluorescence. Across all the biomes, PPIINT in pixels with linear trends showed lower mean values and higher partial correlation coefficients with temperature or precipitation than in pixels without linear trends. Overall, our study revealed the emergence of latitudinal convergence and divergence in climatic controls on the linear and non-linear trends of PPIINT, implying that northern shifts of vegetation and climate change may potentially increase the non-linear nature of climatic controls on ecosystem productivity. These results can improve our understanding and prediction of climate-induced changes in plant phenology and productivity and facilitate sustainable management of ecosystems by accounting for their resilience and vulnerability to future climate change.