Large Geographic Scales Research Articles

Determining the spatial and temporal variability of false killer whales (Pseudorca crassidens) and short-finned pilot whales (Globicephala macrorhynchus) in the Hawaiian Islands from passive acoustic monitoring

False killer whales (Pseudorca crassidens) and short-finned pilot whales (Globicephala macrorhynchus) are resident, toothed whale species to the Hawaiian archipelago. False killer whales are considered of high concern in Hawai‘i with the insular population listed as endangered. Passive acoustic monitoring (PAM) is an effective technique for studying these highly social odontocete species on large temporal and geographical scales. Moored hydrophones were deployed inside and outside Marine Protected Areas (MPAs) through the SanctSound project, allowing leveraging of long-term datasets to assess the acoustic behavior of these species within and beyond the Hawaiian Islands Humpback Whale National Marine Sanctuary and Papahānaumokuākea Marine National Monument. High Frequency Acoustic Recording Packages (HARPs) (200 kHz SR) and SoundTraps (48 kHz SR), recording on various duty cycles, were deployed between 2018–2022 within MPAs (monument: n = 51 months; sanctuary: n = 76 months) and outside MPAs (n = 60 months). PAMGuard detectors were used to detect whistles, clicks, and burst pulses. Detection features were extracted using the PAMpal package and events classified to species using BANTER. Acoustic monitoring provides key information about the value of MPAs and a better understanding of habitat use and behavior of these species.

A Methodology for National Scale Coastal Landcover Mapping in New Zealand

Satellite earth observation data has become fundamental in efforts to map coastal change at large geographic scales. Research has generally focussed on extracting the instantaneous waterline position from time-series of satellite images to interpret long-term trends. The use of this proxy can, however, be uncertain because the waterline is sensitive to marine conditions and beach gradient. In addition, the technique disregards potentially useful data stored in surrounding pixels. In this paper, we describe a pixel-based technique to analyse coastal change. A hybrid rule-based and machine learning methodology was developed using a combination of Sentinel multispectral and Synthetic Aperture Radar composite imagery. The approach was then used to provide the first national-scale pixel-based landcover classification for the open coast of New Zealand. Nine landcover types were identified including vegetation, rock, and sedimentary classes that are common on beaches (dark sand, light sand, and gravel). Accuracy was assessed at national scale (overall accuracy: 86%) and was greater than 90% when normalised for class area. Using a combination of optical and Synthetic Aperture Radar data improved overall accuracy by 14% and enhanced the separation of coastal sedimentary classes. Comparison against a previous classification approach of sandy coasts indicated improvements of 30% in accuracy. The outputs and code are freely available and open-source providing a new framework for per-pixel coastal landcover mapping for all regions where public earth observation data is available.

Integrated spatial models foster complementarity between monitoring programmes in producing large‐scale bottlenose dolphin indicators

AbstractOver the last decades, large‐scale ecological projects have emerged that require collecting ecological data over broad spatial and temporal coverage. Yet, obtaining relevant information about large‐scale population dynamics from a single monitoring programme is challenging, and often several sources of data, possibly heterogeneous, need to be integrated. In this context, integrated models combine multiple data types into a single analysis to quantify the population dynamics of a targeted population. When working at large geographical scales, integrated spatial models have the potential to produce spatialized ecological estimates that would be difficult to obtain if data were analysed separately. In this study, we illustrate how spatial integrated modelling offers a relevant framework for conducting ecological inference at large scales. Focusing on the Mediterranean bottlenose dolphins (Tursiops truncatus), we combined 21,464 km of photo‐identification boat surveys collecting spatial capture–recapture data with 24,624 km of aerial line transect following a distance sampling protocol. We analysed spatial capture–recapture data together with distance sampling data to estimate the abundance and density of bottlenose dolphins. We compared the performances of the distance sampling model and the spatial capture–recapture model fitted independently to our integrated spatial model. The outputs of our spatial integrated models inform bottlenose dolphin ecological status in the French Mediterranean Sea and provide ecological indicators that are required for regional‐scale ecological assessments like the EU Marine Strategy Framework Directive. We argue that integrated spatial models are widely applicable and relevant to conservation research and biodiversity assessment at large spatial scales.

Time-lagged effects of habitat fragmentation on terrestrial mammals in Madagascar.

Biodiversity is severely threatened by habitat destruction. As a consequence of habitat destruction, the remaining habitat becomes more fragmented. This results in time-lagged population extirpations in remaining fragments when these are too small to support populations in the long term. If these time-lagged effects are ignored, the long-term impacts of habitat loss and fragmentation will be underestimated. We quantified the magnitude of time-lagged effects of habitat fragmentation for 157 nonvolant terrestrial mammal species in Madagascar, one of the biodiversity hotspots with the highest rates of habitat loss and fragmentation. We refined species' geographic ranges based on habitat preferences and elevation limits and then estimated which habitat fragments were too small to support a population for at least 100 years given stochastic population fluctuations. We also evaluated whether time-lagged effects would change the threat status of species according to the International Union for the Conservation of Nature (IUCN) Red List assessment framework. We used allometric relationships to obtain the population parameters required to simulate the population dynamics of each species, and we quantified the consequences of uncertainty in these parameter estimates by repeating the analyses with a range of plausible parameter values. Based on the median outcomes, we found that for 34 species (22% of the 157 species) at least 10% of their current habitat contained unviable populations. Eight species (5%) had a higher threat status when accounting for time-lagged effects. Based on 0.95-quantile values, following a precautionary principle, for 108 species (69%) at least 10% of their habitat contained unviable populations, and 51 species (32%) had a higher threat status. Our results highlight the need to preserve continuous habitat and improve connectivity between habitat fragments. Moreover, our findings may help to identify species for which time-lagged effects are most severe and which may thus benefit the most from conservation actions.

Variability of indian monsoon and its forcing mechanisms since late quaternary

The Indian monsoon is an important part of the global monsoon system, allowing important transfers of moisture at a large geographical scale and deeply affecting human populations and economic prosperity of regions. The tropical summer monsoon in the Northern Hemisphere is generally considered to be driven by low latitude solar radiation. Therefore, the summer monsoon strength is near zero-phase to the maximum of Northern Hemisphere Summer Insolation (NHSI). However, records from the Arabian Sea and some other parts of the Indian Ocean (e.g., Andaman Sea) show that a ∼8 kyr phase difference exists between the Indian summer monsoon (ISM) strength and the northern Hemisphere Summer Insolation maxima, which is obviously different from the records of stalagmites in the East Asia and other marine sediments (e.g., Bay of Bengal). This leads to the “sea-land precession phase paradox” in indian summer monsoon research. This paper systematically summarizes the Indian monsoon variability on orbital scale indicated by various records from the Indian monsoon regions (including oceans and continents) since the late Quaternary. The orbital forcing of Indian monsoon, the potential phase difference between indian summer monsoon and northern Hemisphere Summer Insolation and its possible forcing mechanism(s) are further discussed. The observed phase lag between indian summer monsoon and northern Hemisphere Summer Insolation may be controlled by the Atlantic Meridional Overturning Circulation (AMOC), latent heat transfer between the southern Indian Ocean and the Asian continent, or caused by the lack of tightly coupling between the Arabian Sea summer monsoon proxies and the monsoon intensity. In addition, it is still unclear whether previous monsoon proxies can provide a strong constraint on the intensity of summer monsoon. Environmental magnetism has been widely used in high-resolution dating and the analysis of paleoclimate variabilities in marine and terrestrial sediments, due to its high sensitivity on the rainfall and temperature. Therefore, in order to solve these issues, it is necessary to combine magnetic parameters with geochemical and paleontological parameters for more systematic work in the future.

Significant compositional and functional variation reveals the patterns of gut microbiota evolution among the widespread Asian honeybee populations

The gut microbiome is a crucial element that facilitates a host’s adaptation to a changing environment. Compared to the western honeybee Apis mellifera, the Asian honeybee, Apis cerana populations across its natural range remain mostly semi-feral and are less affected by bee management, which provides a good system to investigate how gut microbiota evolve under environmental heterogeneity on large geographic scales. We compared and analyzed the gut microbiomes of 99 Asian honeybees, from genetically diverged populations covering 13 provinces across China. Bacterial composition varied significantly across populations at phylotype, sequence-discrete population (SDP), and strain levels, but with extensive overlaps, indicating that the diversity of microbial community among A. cerana populations is driven by nestedness. Pollen diets were significantly correlated with both the composition and function of the gut microbiome. Core bacteria, Gilliamella and Lactobacillus Firm-5, showed antagonistic turnovers and contributed to the enrichment in carbohydrate transport and metabolism. By feeding and inoculation bioassays, we confirmed that the variations in pollen polysaccharide composition contributed to the trade-off of these core bacteria. Progressive change, i.e., nestedness, is the foundation of gut microbiome evolution among the Asian honeybee. Such a transition during the co-diversification of gut microbiomes is affected by environmental factors, diets in general, and pollen polysaccharides in particular.

Coping with the COVID-19 lockdown: The role of household and family responsibilities in Beijing

The COVID-19 pandemic combined with lockdown measures fundamentally changed urban family life worldwide. This article compares how different household types—singles, couples, nuclear families, and extended families—experienced the lockdown during the first wave of the COVID-19 pandemic in Beijing. It argues that not only the household responsibilities associated with household types at the household level, but also the family responsibilities on a larger geographical scale must be considered to understand the challenges faced by different households under the COVID-19 lockdown in urban China, where intergenerational supports such as family responsibilities to other generations living apart are prevalent during the pre-pandemic stage due to the collectivist culture. This article contributes the lens of household diversity and geographic distance to understanding everyday lives of families under lockdown and to reflect on family transformation in urban China over the past decades more broadly. Through the study of families, it also has implications for neighborhood planning and management aimed at mitigating a pandemic’s negative impacts.

Temporal sampling and network analysis reveal rapid population turnover and dynamic migration pattern in overwintering regions of a cosmopolitan pest.

Genetic makeup of insect pest is informative for source-sink dynamics, spreading of insecticide resistant genes, and effective management. However, collecting samples from field populations without considering temporal resolution and calculating parameters related to historical gene flow may not capture contemporary genetic pattern and metapopulation dynamics of highly dispersive pests. Plutella xylostella (L.), the most widely distributed Lepidopteran pest that developed resistance to almost all current insecticides, migrates heterogeneously across space and time. To investigate its real-time genetic pattern and dynamics, we executed four samplings over two consecutive years across Southern China and Southeast Asia, and constructed population network based on contemporary gene flow. Across 48 populations, genetic structure analysis identified two differentiated insect swarms, of which the one with higher genetic variation was replaced by the other over time. We further inferred gene flow by estimation of kinship relationship and constructed migration network in each sampling time. Interestingly, we found mean migration distance at around 1,000 km. Such distance might have contributed to the formation of step-stone migration and migration circuit over large geographical scale. Probing network clustering across sampling times, we found a dynamic P. xylostella metapopulation with more active migration in spring than in winter, and identified a consistent pattern that some regions are sources (e.g., Yunnan in China, Myanmar and Vietnam) while several others are sinks (e.g., Guangdong and Fujian in China) over 2 years. Rapid turnover of insect swarms and highly dynamic metapopulation highlight the importance of temporal sampling and network analysis in investigation of source-sink relationships and thus effective pest management of P. xylostella, and other highly dispersive insect pests.

Land Cover Change Associated with Unconventional Oil and Gas Development in the Appalachian Region

Unconventional oil and gas (UOG) wells from the Marcellus and Utica shale plays have expanded greatly across the Appalachian region of the United States (US) since the early 2000s. This region is now the single largest natural gas producing area of the US. The local and regional impacts of this industry on the landscape make it critical to understand for future planning efforts. This study investigated land cover change associated with over 21,000 unconventional wells representing 4,240 well pads permitted from 2007 to 2017 in Pennsylvania, West Virginia, and Ohio. The goal was to characterize UOG disturbance to document development patterns and extents in the region. Supervised classification was used to map land use and land-cover changes within a 25-ha buffer of well pads identified in the region. On average, disturbance related to unconventional development impacted 6.2 ha in Pennsylvania, 4.7 ha in Ohio and 4.4 ha in West Virginia and 5.6 ha over the region. Forest and grassland were found to be the most impacted cover types, with increases in impervious surface areas being a significant contributor to land-use classification change. These conversions can contribute to increased forest fragmentation and edge, which can in turn adversely impact biodiversity indicators at the regional level. Additionally, increases in impervious surface in small headwater watersheds can lead to increased sediment and runoff loads in receiving streams. Local and regional land use planning should be implemented during the well pad permit review process to help minimize environmental impacts over larger geographic scales.

Cognition of the manatee: past research and future developments.

In this paper, we present a review of the current knowledge related to the cognitive abilities of the manatee, with a focus on the Antillean manatee in situ and ex situ. Following a biocentric approach, we consider the animals' ecology, perception and sociality and we introduce future perspectives on their cognition. Scientific literature on the cognitive abilities of Antillean manatees' is limited and mainly linked to medical training and veterinary manipulations. To perceive and to interact with their social and natural environment (e.g. social interactions, foraging and traveling), manatees use visual, acoustic and tactile modalities that may be involved in a large range of cognitive abilities. Research on stimuli perception in manatees is scarce; however, these animals demonstrate abilities to learn and appear to show long-term memory. For example, to mate and/or to forage manatees travel at medium and large geographical scales; without doubt their movements entail the use of a set of stimuli and learning processes. Furthermore, their social skills (e.g. social organization, tactile and acoustic communications) are also poorly understood although their social interactions appear to be more complex than previously thought. Finally, as for many animals, temperament/personality may play a key role during their interactions with conspecifics and the environment. These aspects on manatee behavior and cognition are important for management and conservation purposes and help us understand the evolution of these marine mammals.

Combining Climatic and Genomic Data Improves Range-Wide Tree Height Growth Prediction in a Forest Tree.

AbstractPopulation response functions based on climatic and phenotypic data from common gardens have long been the gold standard for predicting quantitative trait variation in new environments. However, prediction accuracy might be enhanced by incorporating genomic information that captures the neutral and adaptive processes behind intrapopulation genetic variation. We used five clonal common gardens containing 34 provenances (523 genotypes) of maritime pine (Pinus pinaster Aiton) to determine whether models combining climatic and genomic data capture the underlying drivers of height growth variation and thus improve predictions at large geographical scales. The plastic component explained most of the height growth variation, probably resulting from population responses to multiple environmental factors. The genetic component stemmed mainly from climate adaptation and the distinct demographic and selective histories of the different maritime pine gene pools. Models combining climate of origin and gene pool of the provenances as well as height-associated positive-effect alleles (PEAs) captured most of the genetic component of height growth and better predicted new provenances compared with the climate-based population response functions. Regionally selected PEAs were better predictors than globally selected PEAs, showing high predictive ability in some environments even when included alone in the models. These results are therefore promising for the future use of genome-based prediction of quantitative traits.

Phenotypic plasticity contributes more to the variations in nutrient resorption than genetic differentiation in a grassland dominant

Abstract Phenotypic plasticity and genetic differentiation are the two important processes determining the leaf nutrient resorption among and within plant species, which is critical for understanding the adaptability of plants. However, relative contributions of these two processes have never been quantified at a large geographical scale. Here, we investigated intraspecific variations in nutrient resorption among 14 Stipa breviflora populations along a latitude gradient in 2018 and 2019. Furthermore, we sow seeds from these populations in two common gardens at different latitudes and examined the variations in nutrient resorption. Our results showed that nitrogen and phosphorus resorption efficiency (NRE and PRE) among S. breviflora populations in nature were positively related to latitude, while this trend disappeared in the common gardens. The heritability of NRE and PRE was 11.45% and 16.78%, respectively. These results suggested that phenotypic plasticity contributed much more than genetic variation to nutrient resorption of S. breviflora. Moreover, the structural equation modelling (SEM) suggested that latitude indirectly affected nutrient resorption mainly by altering soil nutrients. With the increasing of latitude, soil nutrients decreased while nutrient resorption increased. This phenomenon indicated that the main process regulating nutrient resorption is negative feedback to soil nutrient availability. Our study provides new insights into the role of nutrient resorption in plant adaptations to geographic variations. Read the free Plain Language Summary for this article on the Journal blog.

Health Impacts of the Built and Social Environments, and Travel Behavior: The Case of the Sunshine State.

As physical inactivity statistics for the U.S. population show an alarming trend, many health problems have been increasing among Americans in recent decades. Thus, identification of the factors that influence people’s physical activity levels and health outcomes has become ever more essential to promote public health. The built envSFironment is among the main factors that impact individuals’ health outcomes. However, little is known about the health impacts of built environment factors at large geographical scales such as those of the metropolitan area of residence. Further, the health impacts of travel behavior such as telecommuting and teleshopping remain unclear. This study uses an ecological model framework to probe the roles of travel behavior and built as well as social environments at different spatial levels in health. Instrumental variable binary probit models have been developed to examine the complex interlinks between measures of travel behavior, physical activity levels, built and social environment characteristics, and individuals’ health outcomes. Findings indicate that built and social environment factors at different spatial levels, including the metropolitan area, are correlated with individuals’ health outcomes. Additionally, the findings suggest that increased levels of telecommuting and teleshopping within communities may lead to unfavorable health outcomes. The findings shed light on the most promising policy interventions that can promote public health through modifications targeting people’s travel choices as well as the built and social environments within urban areas.

Foraging range scales with colony size in high-latitude seabirds

Density-dependent prey depletion around breeding colonies has long been considered an important factor controlling the population dynamics of colonial animals.1-4 Ashmole proposed that as seabird colony size increases, intraspecific competition leads to declines in reproductive success, as breeding adults must spend more time and energy to find prey farther from the colony.1 Seabird colony size often varies over several orders of magnitude within the same species and can include millions of individuals per colony.5,6 As such, colony size likely plays an important role in determining the individual behavior of its members and how the colony interacts with the surrounding environment.6 Using tracking data from murres (Uria spp.), the world's most densely breeding seabirds, we show that the distribution of foraging-trip distances scales to colony size0.33 during the chick-rearing stage, consistent with Ashmole's halo theory.1,2 This pattern occurred across colonies varying in size over three orders of magnitude and distributed throughout the North Atlantic region. The strong relationship between colony size and foraging range means that the foraging areas of some colonial species can be estimated from colony sizes, which is more practical to measure over a large geographic scale. Two-thirds of the North Atlantic murre population breed at the 16 largest colonies; by extrapolating the predicted foraging ranges to sites without tracking data, we show that only two of these large colonies have significant coverage as marine protected areas. Our results are an important example of how theoretical models, in this case, Ashmole's version of central-place-foraging theory, can be applied to inform conservation and management in colonial breeding species.

Sturgeons in large rivers: detecting the near-extinct needles in a haystack via eDNA metabarcoding from water samples

Sturgeon populations are declining worldwide and are the target of extensive conservation efforts. Addressed in several pieces of legislation, sturgeons have received considerable attention as flagship or umbrella species. Despite the need for a better understanding of the distribution and population status, the use of traditional sampling methods failed in the past, thereby hampering reliable assessments, a prerequisite for conservation. Here, we describe the development and application of an environmental DNA (eDNA) metabarcoding approach for detecting rare sturgeons in large rivers. Exemplarily, we developed a reference database for five native Danube sturgeons (Acipenser stellatus, Acipenser gueldenstaedtii, Acipenser ruthenus, Acipenser nudiventris, and Huso huso) and two non-native species (Acipenser baerii and Acipenser transmontanus), assessed these ex situ, and used eDNA as a detection tool along the entire length of the Danube (Europe, ~ 2850 km) and major tributaries. In ex situ analyses, all assays yielded positive amplifications for the assessed sturgeon species. In the Danube, the presence of A. ruthenus was confirmed at 14 of 29 sites (48.3%), and in 2 of 18 tributary sites (11.1%), providing the first comprehensive large-scale biogeographical snapshot of this species. Relative number of reads assigned to A. ruthenus varied between 0 and 2.5%, with sites registering positive detections being clustered in 3 sections of the Danube. Our findings enabled us to confirm the advantages of eDNA monitoring over traditional sampling methods for comprehensive whole-river snapshot studies of sturgeons conducted on a large geographical scale, and therefore we consider it to be a promising approach for application in conservation measures, fisheries management, scientific studies, and adaptive management plans for sturgeons on a global scale.

Traffic Event Detection from Consumer Vehicle Sensor Data: An Autonomous Vehicle Study

Traffic event detection from vehicles’ on-board sensors can detect the road information and improve traffic management and safety. Current sensor-based traffic event detection is mainly based on probe vehicles, test vehicles, or other designated vehicles, which is costly and cannot be deployed on a large scale. With the fast development of on-board equipment, data collection from consumer vehicle sensors is becoming popular and can cover a large geographic scale with almost no equipment or labor cost. However, there are very few studies of the data features and potential for application. This paper presents a pipeline for employing consumer vehicle sensors to detect roadworks. The unique data features and deficiencies of the consumer vehicle sensor are discussed and summarized. A clustering method is employed to distinguish the roadwork sites. A route builder method is proposed to reconstruct the routes of the roadworks and to extract the corresponding start and end locations. Compared with ground truth, roadworks detection from consumer vehicle sensors can cover up to 86% of the roadworks on freeways and over 40% of the roadworks on non-freeway roads. The average offset errors are 4.7% on freeways, and 24.9% on non-freeway roads. Compared with point-based roadworks detection from “ViaMichelin Traffic information,” the results from consumer vehicle sensors achieved a matching ratio of nearly 90% and were advantageous in extracting the roadworks route information. This study proves the possibility of employing consumer vehicle sensors for route-based traffic event detection and provides insights for countering uneven distribution and trajectory truncation issues related to privacy protection.

Predicting Seabird Foraging Habitat for Conservation Planning in Atlantic Canada: Integrating Telemetry and Survey Data Across Thousands of Colonies

Conservation of mobile organisms is difficult in the absence of detailed information about movement and habitat use. While the miniaturization of tracking devices has eased the collection of such information, it remains logistically and financially difficult to track a wide range of species across a large geographic scale. Predictive distribution models can be used to fill this gap by integrating both telemetry and census data to construct distribution maps and inform conservation goals and planning. We used tracking data from 520 individuals of 14 seabird species in Atlantic Canada to first compare foraging range and distance to shorelines among species across colonies, and then developed tree-based machine-learning models to predict foraging distributions for more than 5000 breeding sites distributed along more than 5000 km of shoreline. Despite large variability in foraging ranges among species, tracking data revealed clusters of species using similar foraging habitats (e.g., nearshore vs. offshore foragers), and within species, foraging range was highly colony-specific. Even with this variability, distance from the nesting colony was an important predictor of distribution for nearly all species, while distance from coastlines and bathymetry (slope and ruggedness) were additional important predictors for some species. Overall, we demonstrated the utility of tree-based machine-learning approach when modeling tracking data to predict distributions at un-sampled colonies. Although tracking and colony data have some shortcomings (e.g., fewer data for some species), where results need to be interpreted with care in some cases, applying methods for modeling breeding season distributions of seabirds allows for broader-scale conservation assessment. The modeled distributions can be used in decisions about planning for offshore recreation and commercial activities and to inform conservation planning at regional scales.

A generalizable framework for spatially explicit exploration of soil organic carbon sequestration on global marginal land

Land-based CO2 removal demands changes in management or new suitable areas to sustainably grow additional biomass without reducing food supply or damaging natural ecosystems. The soil organic carbon (SOC) sequestration pathway is thought to transfer atmospheric CO2 into a land unit, through plants, plant residues and other organic solids stored as part of the soil organic matter. No previous study explored SOC sequestration potentials on global marginal land. Here we integrated, into a generalizable modelling framework, the mapping of a set of biophysical (climatic and edaphic) and land conservation constraints to (i) identify suitable matches (i.e. biophysically possible combinations) of target areas with plant species, and (ii) to quantify contributions of pairing to long-term SOC sequestration (2020–2100). The proposed framework represents a refinement to previous mapping exercises, which seldom consider biophysical constraints, soil erosion, plant species tolerances to pedoclimatic conditions, and world protected areas. The approach was tested on marginal lands featuring SOC-deficient stocks (≤ 50 Mg SOC ha−1 to 30 cm depth) at 30 arc-sec resolution, consolidated into world regions × global ecological zones based on geo-localised products. The framework was shown to enable better-informed decision-making on interventions at large geographical scales, revealing biophysically realistic options, while management should be determined locally.

A comparative study of regional climate trends in the Keoladeo and Bhitarkanika wetlands, India

The climate trend is one of the most dominant factors likely to affect the structure and function of the wetlands of Keoladeo and Bhitarkanika. This study compares the climate conditions and trends of two Ramsar sites in India, the Bhitarkanika and Keoladeo wetlands. Forty-year climate data (1980-2020) were used to identify and measure the strength of possible climate change in these habitats. Mann-Kendall’s test, Sen’s slope estimates and Sequential Mann-Kendall’s (SQMK) test were used to study the long-term and short-term trends of rainfall and climate of Bhitarkanika and Keoladeo. The results of this study matched those of earlier studies conducted at a larger geographical scale. The study indicated significant changes in the parameters of rainfall and temperature trends. A significant increase in annual rainfall (Sen’s slope = 5.24E+00, New p value = 2.75E-02) was observed at Bhitarkanika, while the trend at Keoladeo was positive but insignificant. Both ecosystems are getting warmer. Seasonal changes were also identified in the rainfall and temperature. The summer temperature rise was comparatively stronger at Keoladeo (Tmax: Sen’s slope=2.33E-02, New p value=2.45E-02; Tmin: Sen’s slope=2.68E-02, New p value=1.39E-02). An increase in monsoon temperature was also recorded in both ecosystems. Both wetland ecosystems are vulnerable to climate change, but the Keoladeo wetland is at higher risk because stronger temperature changes have occurred in the past four decades. The SQMK test also suggests a more erratic climate in the Keoladeo wetlands. The study highlights the comparative changes occurring in both ecosystems. This is the first study identifying both long-term and short-term changes in these wetlands at a smaller geographical scale.

Physics-based sizing functions for ocean-to-estuary shallow-water models

Modeling ocean processes requires a discrete representation of the system geometry, which itself contains multiple degrees of freedom, especially for systems of large geographical scale. This paper assesses the unstructured meshing requirements for a large-scale, astronomic-tide model domain of the western North Atlantic Ocean, Gulf of Mexico and Caribbean Sea with telescopic focus of the South Atlantic Bight (United States southeastern seaboard) and its estuaries. The methodologies of localized truncation error analysis (LTEA), which account only for the model interior and LTEA plus complex derivatives (+CD), which account for the model interior and boundary, are exploited due to their foundations stemming directly from tidal physics. LTEA and LTEA+CD are applied for the M2 tidal harmonics resulting from linear and nonlinear solutions of the ADCIRC (advanced circulation) model based on a highly resolved uniform mesh. The resultant target element size distributions are interrogated distinctively for the inshore (estuarine) region of the South Atlantic Bight, the inlet-punctuated coastline, the shelf-slope-rise triad and deep ocean, and the open-ocean boundary. The relatively large size of the LTEA computational molecule makes it the suitable choice for the deep ocean, offshore and shelf regions where the tidal dynamics are predominantly linear and smoothly changing in space. LTEA+CD is the logical choice for the shallows and estuaries because of the spatial coverage afforded by the combined utility of interior and boundary estimators (computational molecules), as well as its accounting for the nonlinearities in the evaluation of local truncation error. The combination of the two methods enables an almost complete coverage of the domain for generating spacing requirements, with LTEA+CD extending beyond the interior-only definition of LTEA to capture the inshore regions and model boundaries. Implementing the approach with LTEA and LTEA+CD to target finer resolution in areas with sharper gradients in the hydrodynamics, while relaxing resolution in areas with smoother hydrodynamics, resulted in a mesh with a far-reduced number of elements (∼1.3M) relative to an overly resolved uniform mesh (>10M elements) that is comparable in accuracy with ±5% tidal amplitude error for 99% of the domain.