Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region

Climate change causes declines and greater extremes in wetland inundation in a region important for wetland birds

Wetland ecosystems are vital for maintaining global biodiversity, as they provide important stopover sites for many species of migrating wetland-associated birds. However, because weather determines their hydrologic cycles, wetlands are highly vulnerable to effects of climate change. Although changes in temperature and precipitation resulting from climate change are expected to reduce inundation of wetlands, few efforts have been made to quantify how these changes will influence the availability of stopover sites for migratory wetland birds. Additionally, few studies have evaluated how climate change will influence interannual variability or the frequency of extremes in wetland availability. For spring and fall bird migration in seven ecoregions in the south-central Great Plains of North America, we developed predictive models associating abundance of inundated wetlands with a suite of weather and land cover variables. We then used these models to generate predictions of wetland inundation at the end of the century (2069-2099) under future climate change scenarios. Climate models predicted the average number of inundated wetlands will likely decline during both spring and fall migration periods, with declines being greatest in the eastern ecoregions of the southern Great Plains. However, the magnitude of predicted declines varied considerably across climate models and ecoregions, with uncertainty among climate models being greatest in the High Plains ecoregion. Most ecoregions also were predicted to experience more-frequent extremely dry years (i.e., years with extremely low wetland abundances), but the projected change in interannual variability of wetland inundation was relatively small and varied across ecoregions and seasons. Because the south-central Great Plains represents an important link along the migratory routes of many wetland-dependent avian species, future declines in wetland inundation and more frequent periods of only a few wetlands being inundated will result in an uncertain future for migratory birds as they experience reduced availability of wetland stopover habitat across their migration pathways.

Adaptable leadership for arid/semi-arid wetlands conservation under climate change: Using Analytical Hierarchy Process (AHP) approach

Adverse socio-economic and environmental impacts of climate change on wetlands have enforced the international community and many nations to develop adaptive policies for wetland management, which require effective leadership to influence relevant stakeholders. This study identifies and prioritizes leadership functions and theories for climate change adaptation (CCA) in wetlands ecosystems, particularly in arid and semi-arid regions. A mixed qualitative-quantitative research methodology was applied through focus groups and a survey with a sample of national, sub-national, and local experts on wetlands management and climate change in Iran. The Analytic Hierarchy Processing (AHP) technique identified the political-administrative (weight = 0.245), adaptive (W = 0.244), and enabling (W = 0.237) functions for CCA, respectively, as three prioritized leadership functions, followed by the dissemination function (W = 0.102), which emphasizes the necessity of applying and enhancing leaders' social capacities, knowledge, communication skills, and personal networks to facilitate social learning and actions regarding CCA in local communities and among relevant organizations. It is necessary to overcome structural and functional barriers for leaders and their followers to information access and involvement in participatory decision-making platforms. Moreover, network and communication leadership theories (W = 0.368) and sustainable leadership perspectives (W = 0.362), respectively, have the highest priority among leadership theories and are crucial for establishing participatory decision-making among relevant stakeholders and applying adaptive strategies for wetlands governance under climate change conditions. The reconceptualization of leadership as a complex adaptive notion draws attention to the social complexities and emerging characteristics of leadership in contemporary societies and organizations. The understanding of leaders' and followers' networks and identifying the core role of leaders provides a foundation for developing leadership functions and theories beyond hierarchical, individualistic, and one-way concepts of leadership.

Driving Force Analysis of Natural Wetland in Northeast Plain Based on SSA-XGBoost Model

Globally, natural wetlands have suffered severe ecological degradation (vegetation, soil, and biotic community) due to multiple factors. Understanding the spatiotemporal dynamics and driving forces of natural wetlands is the key to natural wetlands' protection and regional restoration. In this study, we first investigated the spatiotemporal evolutionary trends and shifting characteristics of natural wetlands in the Northeast Plain of China from 1990 to 2020. A dataset of driving-force evaluation indicators was constructed with nine indirect (elevation, temperature, road network, etc.) and four direct influencing factors (dryland, paddy field, woodland, grassland). Finally, we built the driving force analysis model of natural wetlands changes to quantitatively refine the contribution of different driving factors for natural wetlands' dynamic change by introducing the sparrow search algorithm (SSA) and extreme gradient boosting algorithm (XGBoost). The results showed that the total area of natural wetlands in the Northeast Plain of China increased by 32% from 1990 to 2020, mainly showing a first decline and then an increasing trend. Combined with the results of transfer intensity, we found that the substantial turn-out phenomenon of natural wetlands occurred in 2000-2005 and was mainly concentrated in the central and eastern parts of the Northeast Plain, while the substantial turn-in phenomenon of 2005-2010 was mainly located in the northeast of the study area. Compared with a traditional regression model, the SSA-XGBoost model not only weakened the multicollinearity of each driver but also significantly improved the generalization ability and interpretability of the model. The coefficient of determination (R2) of the SSA-XGBoost model exceeded 0.6 in both the natural wetland decline and rise cycles, which could effectively quantify the contribution of each driving factor. From the results of the model calculations, agricultural activities consisting of dryland and paddy fields during the entire cycle of natural wetland change were the main driving factors, with relative contributions of 18.59% and 15.40%, respectively. Both meteorological (temperature, precipitation) and topographic factors (elevation, slope) had a driving role in the spatiotemporal variation of natural wetlands. The gross domestic product (GDP) had the lowest contribution to natural wetlands' variation. This study provides a new method of quantitative analysis based on machine learning theory for determining the causes of natural wetland changes; it can be applied to large spatial scale areas, which is essential for a rapid monitoring of natural wetlands' resources and an accurate decision-making on the ecological environment's security.

Shifts in the Distribution Range and Niche Dynamics of the Globally Threatened Western Tragopan (Tragopan melanocephalus) Due to Climate Change and Human Population Pressure

The impact of a changing climate, particularly global warming, often harms the distribution of pheasants, particularly those with limited endemic ranges. To effectively create plans of action aimed at conserving species facing threats such as the Western Tragopan, (Tragopan melanocephalus; Gray, 1829; Galliformes, found in the western Himalayas), it is crucial to understand how future distributions may be affected by anticipated climate change. This study utilized MaxEnt modeling to assess how suitable the habitat of the targeted species is likely to be under different climate scenarios. While similar studies have been conducted regionally, there has been no research on this particular endemic animal species found in the western Himalayas throughout the entire distribution range. The study utilized a total of 200 occurrence points; 19 bioclimatic, four anthropogenic, three topographic, and a vegetation variable were also used. To determine the most fitting model, species distribution modeling (SDM) was employed, and the MaxEnt calibration and optimization techniques were utilized. Data for projected climate scenarios of the 2050s and 2070s were obtained from SSPs 245 and SSPs 585. Among all the variables analyzed; aspect, precipitation of coldest quarter, mean diurnal range, enhanced vegetation index, precipitation of driest month, temperature seasonality, annual precipitation, human footprint, precipitation of driest quarter, and temperature annual range were recognized as the most influential drivers, in that order. The predicted scenarios had high accuracy values (AUC-ROC > 0.9). Based on the feedback provided by the inhabitants, it was observed that the livability of the selected species could potentially rise (between 3.7 to 13%) in all projected scenarios of climate change, because this species is relocating towards the northern regions of the elevation gradient, which is farther from the residential areas, and their habitats are shrinking. The suitable habitats of the Tragopan melanocephalus in the Himalayan region will move significantly by 725 m upwards, because of predicted climate change. However, the fact that the species is considered extinct in most areas and only found in small patches suggests that further research is required to avert a further population decline and delineate the reasons leading to the regional extinction of the species. The results of this study can serve as a foundation for devising conservation strategies for Tragopan melanocephalus under the changing climate and provide a framework for subsequent surveillance efforts aimed at protecting the species.

Climate Change and Human Activities, the Significant Dynamic Drivers of Himalayan Goral Distribution (Naemorhedus goral)

The distribution of large ungulates is more often negatively impacted by the changing climate, especially global warming and species with limited distributional zones. While developing conservation action plans for the threatened species such as the Himalayan goral (Naemorhedus goral Hardwicke 1825; a mountain goat that mostly inhabits rocky cliffs), it is imperative to comprehend how future distributions might vary based on predicted climate change. In this work, MaxEnt modeling was employed to assess the habitat suitability of the target species under varying climate scenarios. Such studies have provided highly useful information but to date no such research work has been conducted that considers this endemic animal species of the Himalayas. A total of 81 species presence points, 19 bioclimatic and 3 topographic variables were employed in the species distribution modeling (SDM), and MaxEnt calibration and optimization were performed to select the best candidate model. For predicted climate scenarios, the future data is drawn from SSPs 245 and SSPs 585 of the 2050s and 2070s. Out of total 20 variables, annual precipitation, elevation, precipitation of driest month, slope aspect, minimum temperature of coldest month, slope, precipitation of warmest quarter, and temperature annual range (in order) were detected as the most influential drivers. A high accuracy value (AUC-ROC > 0.9) was observed for all the predicted scenarios. The habitat suitability of the targeted species might expand (about 3.7 to 13%) under all the future climate change scenarios. The same is evident according to local residents as species which are locally considered extinct in most of the area, might be shifting northwards along the elevation gradient away from human settlements. This study recommends additional research is conducted to prevent potential population collapses, and to identify other possible causes of local extinction events. Our findings will aid in formulating conservation plans for the Himalayan goral in a changing climate and serve as a basis for future monitoring of the species.

Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia

One of the potential impacts of climate change is enhanced groundwater contamination by geogenic and anthropogenic contaminants. Such impacts should be most evident in areas with high land-use change footprint. Here, we provide a novel documentation of the impact on groundwater nitrate (GWNO3 ) pollution with and without climate change in one of the most intensely groundwater-irrigated areas of South Asia (northwest India) as a consequence of changes in land use and agricultural practices at present and predicted future times. We assessed the probabilistic risk of GWNO3 pollution considering climate changes under two representative concentration pathways (RCPs), i.e., RCP 4.5 and 8.5 for 2030 and 2040, using a machine learning (Random Forest) framework. We also evaluated variations in GWNO3 distribution against a no climate change (NCC) scenario considering 2020 status quo climate conditions. The climate change projections showed that the annual temperatures would rise under both RCPs. The precipitation is predicted to rise by 5% under RCP 8.5 by 2040, while it would decline under RCP 4.5. The predicted scenarios indicate that the areas at high risk of GWNO3 pollution will increase to 49 and 50% in 2030 and 66 and 65% in 2040 under RCP 4.5 and 8.5, respectively. These predictions are higher compared to the NCC condition (43% in 2030 and 60% in 2040). However, the areas at high risk can decrease significantly by 2040 with restricted fertilizer usage, especially under the RCP 8.5 scenario. The risk maps identified the central, south, and southeastern parts of the study area to be at persistent high risk of GWNO3 pollution. The outcomes show that the climate factors may impose a significant influence on the GWNO3 pollution, and if fertilizer inputs and land uses are not managed properly, future climate change scenarios can critically impact the groundwater quality in highly agrarian areas.

Assessment of Climate Change and Land Use Effects on Water Lily (Nymphaea L.) Habitat Suitability in South America

Many aquatic species have restricted dispersal capabilities, making them the most vulnerable organisms to climate change and land use change patterns. These factors deplete Nymphaea species’ suitable habitats, threatening their populations and survival. In addition, the species are poorly documented, which may indicate how scarce they are or will become. Members of Nymphaea are ecologically important as well as having cultural and economic value, making them of conservation interest. Therefore, using the maximum entropy (MaxEnt) approach, climatic variables, land use, and presence points were modeled for seven Nymphaea species in South America, using three general circulation models (CCSM4, HADGEM2-AO, and MIROC5) and in two representative concentration pathways (RCPs 4.5 and 8.5) and two scenarios (2050 and 2070). Our results indicated that mean diurnal range (bio2), precipitation of the wettest month (bio13), temperature seasonality (bio15), and land use (dom_lu) were the main influencing factors. For all species, suitable areas were concentrated east of Brazil, and they were variable in northern parts of the continent. Besides, inconsistent expansion and contraction of suitable habitats were noticed among the species. For example, N. amazonum, N. rudgeana, and N. lasiophylla future habitat expansions declined and habitat contraction increased, while for N. ampla and N. jamesoniana, both future habitat expansion and contraction increased, and for N. pulchella and N. rudgeana it varied in the RCPs. Moreover, the largest projected suitable habitats were projected outside protected areas, characterized by high human impacts, despite our analysis indicating no significant change between protected and unprotected areas in suitable habitat change. Finally, understanding how climate change and land use affect species distribution is critical to developing conservation measures for aquatic species.

Population Scale Analysis of Centromeric Satellite DNA Reveals Highly Dynamic Evolutionary Patterns and Genomic Organization in Long-Tailed and Rhesus Macaques

Centromeric satellite DNA (cen-satDNA) consists of highly divergent repeat monomers, each approximately 171 base pairs in length. Here, we investigated the genetic diversity in the centromeric region of two primate species: long-tailed (Macaca fascicularis) and rhesus (Macaca mulatta) macaques. Fluorescence in situ hybridization and bioinformatic analysis showed the chromosome-specific organization and dynamic nature of cen-satDNAsequences, and their substantial diversity, with distinct subfamilies across macaque populations, suggesting increased turnovers. Comparative genomics identified high level polymorphisms spanning a 120 bp deletion region and a remarkable interspecific variability in cen-satDNA size and structure. Population structure analysis detected admixture patterns within populations, indicating their high divergence and rapid evolution. However, differences in cen-satDNA profiles appear to not be involved in hybrid incompatibility between the two species. Our study provides a genomic landscape of centromeric repeats in wild macaques and opens new avenues for exploring their impact on the adaptive evolution and speciation of primates.

Modeling Current and Future Distribution of Epicauta Dejean (Meloinae, Epicautini) under Changing Climate Conditions in America

Epicauta Dejean is one of the largest genera within Meloidae, with approximately 400 species identified to date. In this work, I applied the maximum entropy algorithm (Maxent) to predict the current and future distribution of this genus in America. A total of 12,130 points and 19 bioclimatic variables were used to model its potential distribution area under current and future climate scenarios. Maxent showed high prediction performance, and 7 out of the 19 variables used were found to be the most influential on the current and future distribution of Epicauta. It also allowed to predict the distribution of Epicauta in geographical areas where different bioclimatic criteria are combined. These areas belong to several provinces of the Nearctic, Neotropical regions and the Mexican and South American transition zones. Maxent also revealed that in North America, the current and future potential distribution of Epicauta is located within 38°N 97°W, while in South America, it is further south, within 25°S 60°W. According to this, it can be concluded that its greatest diversity is circumscribed to temperate and semi-arid regions, and that the tropical habitats of middle America have apparently served as effective barriers to faunal exchange since the intercontinental connection that occurred four million years ago until now. The findings from the present study provide a theoretical basis to better understand the distribution patterns of Epicauta spp. under changing climate conditions.

Habitat potential modelling and mapping of Teucrium polium using machine learning techniques

Determining suitable habitats is important for the successful management and conservation of plant and wildlife species. Teucrium polium L. is a wild plant species found in Iran. It is widely used to treat numerous health problems. The range of this plant is shrinking due to habitat destruction and overexploitation. Therefore, habitat suitability (HS) modeling is critical for conservation. HS modeling can also identify the key characteristics of habitats that support this species. This study models the habitats of T. polium using five data mining models: random forest (RF), flexible discriminant analysis (FDA), multivariate adaptive regression splines (MARS), support vector machine (SVM), and generalized linear model (GLM). A total of 119 T. poliumlocations were identified and mapped. According to the RF model, the most important factors describing T. polium habitat were elevation, soil texture, and mean annual rainfall. HS maps (HSMs) were prepared, and habitat suitability was classified as low, medium, high, or very high. The percentages of the study area assigned high or very high suitability ratings by each of the models were 44.62% for FDA, 43.75% for GLM, 43.12% for SVM, 38.91% for RF, 28.72% for MARS, and 39.16% for their ensemble. Although the six models were reasonably accurate, the ensemble model had the highest AUC value, demonstrating a strong predictive performance. The rank order of the other models in this regard is RF, MARS, SVM, FDA, and GLM. HSMs can provide useful output to support the sustainable management of rangelands, reclamation, and land protection.

Study of marsh wetland landscape pattern evolution on the Zoigê Plateau due to natural/human dual-effects

Zoigê Plateau, China’s largest plateau marsh wetland, has experienced large-scale degradation of the marsh wetland and evolution of the wetland landscape pattern over the past 40 years due to climate warming and human activities. How exactly do the wetland landscape pattern characteristics change? How do climatic change and human activities affect the wetland evolution? These questions are yet to be systematically investigated. In order to investigate changes to the marsh wetland on the Zoigê Plateau, field investigations, spatial and statistical analysis were undertaken. Findings from our study indicate that from 1977–2016, the area of marsh wetland on the Plateau reduced by 56.54%, approximately 66,700 hm² of marsh wetland has been lost. The centroids of both marsh and marshy meadow migrated and the landscape centroid migration behaviors were also correlated with the distribution and variation of the marsh wetland on different slopes. In addition, the number of marsh landscape patches initially increased before decreasing; the number of marshy meadow landscape patches also recorded an initial increase, followed by a decline before a final increase. As the effects of human activities weakened, the aggregation degrees of both marsh and marshy meadow increased. Overall, the fragmentation degree, diversity and fractal dimension of the marsh wetland all declined. An investigation into the driving factors affecting the Plateau area shows that the increase of annual average temperature was the natural factor while trenching and overgrazing were the main human factors resulting in wetland degradation. Results from this study provide basic data and theoretical foundation for the protection and restoration of marsh wetland in alpine regions.

MaxEnt Modeling for Predicting the Current and Future Potential Geographical Distribution of Quercus libani Olivier

The purpose of the study was to model the current and potential future distribution of Quercus libani Olivier (Lebanon Oak), a tree species in Turkey, and to predict the changes in its geographical distribution under different climate change scenarios. In this study, 19 bioclimatic variables at a spatial resolution of 30 arc seconds (~1 km2) were used, collected from the WorldClim database. The bioclimatic data with high correlation according to 31 sets of presence data on the species were reduced with principal component analysis (PCA), and the current and potential distribution were identified using MaxEnt 3.4.1 software. In order to predict how the distribution of the species will be affected by climate change, its potential geographical distribution by 2050 and 2070 was modeled under the Representative Concentration Pathways (RCP) RCP 4.5 and RCP 8.5 scenarios of the species using the Community Climate System Model (CCSM, version 4), which is a climate change model based on the report of the fifth Intergovernmental Panel on Climate Change (IPCC). Change analysis was performed to determine the spatial differences between its current and future distribution areas. The study results showed that the suitable areas for the current distribution of Quercus libani Olivier cover 72,819 km2. Depending on the CCSM4 climate model, the suitable area will decline to 67,580 km2 by 2070, according to the RCP 4.5 scenario, or 63,390 km2 in the RCP 8.5 scenario. This may lead to a reduction in the future population of this species. The change analysis showed that suitable and highly suitable areas will decrease under global climate change scenarios (RCP 4.5 and RCP 8.5) for both current and future potential distribution areas. In this context, our study results indicate that for the management of this species, protective environmental measures should be taken, and climate change models need to be considered in land use and forest management planning.

Quantifying the Relative Importance of Climate Change and Human Activities on Selected Wetland Ecosystems in China

Climate change and human activities are important factors driving changes in wetland ecosystems. It is therefore crucial to quantitatively characterize the relative importance of these stressors in wetlands. Previous such analyses have generally not distinguished between wetland types, or have focused on individual wetland types. In this study, three representative wetland areas of the upper, middle and lower reaches of the Heilongjiang River Basin (HRB) were selected as the study area. An object-based classification was used with Landsat TM data to extract the spatial distribution of wetland in 1990, 2000 and 2010. We then quantified the relative importance of climate change and human activities on the wetlands by using the R package “relaimpo” package. The results indicated that: (1) the effects of human activities on wetland changes were greater (contribution rate of 63.57%) than climate change in the HRB. Specifically, there were differences in the relative importance of climate change and human activities for wetlands in different regions. Wetlands of the upper reaches were more affected by climate change, while wetlands in the middle and lower reaches were more affected by human activities; (2) climate change had a greater impact (contribution rate of 65.72%) on low intensity wetland loss, while human activities had a greater impact on moderate and severe intensity wetland loss, with respective contribution rates of 57.22% and 70.35%; (3) climate change had a larger effect on the shrub and forested wetland changes, with respective contribution rates of 58.33% and 52.58%. However, human activities had a larger effect on herbaceous wetland changes, with a contribution rate of 72.28%. Our study provides a useful framework for wetland assessment and management, and could be a useful tool for developing wetland utilization and protection approaches, particularly in sensitive environments in mid- and high-latitude areas.

Modeling Intersecting Processes of Wetland Shrinkage and Urban Expansion by a Time-Varying Methodology

Continuous urban expansion worldwide has resulted in significant wetland degradation and loss. A limited number of studies have addressed the coupling of wetland and urban dynamics, but this relationship remains unclear. In this study, a time-varying methodology of predicting wetland distribution was developed to support decision-making. The novelty of the methodology is its ability to dynamically simulate wetland shrinkage together with urban expansion and reveal conflicts and potential tradeoffs under different scenarios. The developed methodology consists of three modules: a historical change detection of wetland and urban areas module, a spatial urban sprawl simulation and forecasting module that can accommodate different development priorities, and a wetland distribution module with time-varying logistic regression. The methodology was applied and tested in the Tonghu Wetland as a case study. The wetland and urban extents presented a spatially intersecting shift, where wetlands lost more than 40% of their area from 1977 to 2017, while urban areas expanded by 10-fold, threatening wetlands. The increase in the relative importance metric of the time-varying regression model indicated an enhanced influence of urban expansion on the wetland. An accuracy assessment validated a robust statistical result and a good visual fit between spatially distributed wetland occurrence probabilities and the actual distribution of wetland. Incorporating the new variable of urban expansion improved modeling performance and, particularly, realized a greater ability to predict potential wetland loss than provided by the traditional method. Future wetland loss probabilities were visualized under different scenarios. The historical trend scenario predicted continuously expanding urban growth and wetland shrinkage to 2030. However, a specific urban development strategy scenario was designed interactively to control the potential wetland loss. Consideration of such scenarios can facilitate identifying tradeoffs to support wetland conservation.

Distribution and Driving Factors of Forest Swamp Conversions in a Cold Temperate Region

Forest swamps are widely distributed in cold temperate regions, with important landscape and ecological functions. They are prone to conversion caused by complex factors. Forest swamp conversions involve forest swamping, meadow swamping, water body swamping, and conversion to farmland. An understanding of the landscape characteristics and primary environmental factors driving forest swamp conversions is imperative for exploring the mechanism of forest swamp conversions. We investigated the landscape characteristics of forest swamp conversions and quantified the relative importance of environmental factors driving these conversions for the period from 1990 to 2015 in the Great Xing’an Mountains of China. We found that forest swamping displayed high patch numbers (34,916) and density (8.51/100 ha), commonly occurring at the edge of large areas of forests. Meadow swamping was localized with low patch numbers (3613) and density (0.88/100 ha) due to lack of water recharge from ground water. Water body swamping had complex shapes (perimeter area ratio mean = 348.32) because of water table fluctuations and helophyte growth during this conversion process. Conversions to farmland presented fairly regular (perimeter area ratio mean = 289.91) and aggregated (aggregation index = 67.82) characteristics affected by agricultural irrigation and management. We found that climatic and geomorphic factors were relatively important compared to topographic factors for forest swamp conversions. Negative geomorphic conditions provided the waterlogging environment as a precondition of swamp formation. Sufficient precipitation was an important source of water recharge due to the existence of permafrost regions and long-term low temperature reduced the evaporation of swamps water and the decomposition rate of organisms. These wet and cold climatic conditions promoted forest swamp development in cold temperate regions. Humans exerted a relatively important role in forest swamping and conversions to farmland. Fire disturbance and logging accelerated the conversion from forest to swamp. This study provides scientific information necessary for the management and conservation of forest swamp resources in cold temperate regions.