{"paper_id":"080804f4-f474-4e93-8e1c-d5009ccd09d0","body_text":"Spatiotemporal Variation of Small and Micro Wetlands and Their Multiple Responses to Driving Factors in the high-latitude region | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal Variation of Small and Micro Wetlands and Their Multiple Responses to Driving Factors in the high-latitude region Yingbin Wang, Jiaxin Sun, Yao Wu, Peng Qi, Wenguang Zhang, Yongming Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4003007/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Dec, 2024 Read the published version in Wetlands → Version 1 posted 5 You are reading this latest preprint version Abstract Understanding the long-term dynamics and driving factors of small and micro wetlands is crucial for their management and sustainable development. In this study, we utilized Landsat satellite imagery data from 1980 to 2018 and Geodetector Model to explore the spatiotemporal changes of small and micro wetlands in western Jilin Province, China, considering factors such as land use and climate change. The research findings are as follows. (1) The small and micro wetlands in the western Jilin Province have shown a downward trend in the past 40 years. The area of small and micro wetlands has decreased from 2802km 2 in 1980 to 818 km 2 in 2018, and the number has decreased from 2604 in 1980 to 337 in 2018. (2) From a spatial distribution perspective, the micro-wetlands initially exhibited a concentrated pattern but gradually dispersed around, demonstrating significant spatial heterogeneity., respectively. From a spatial distribution perspective, they are mainly distributed in Da'an City, accounting for 42% of the western Jilin province. (3) As time has unfolded, the dynamic evolution of small and micro wetlands has been distinctly influenced by an amalgam of natural environmental factors and human interventions. In particular, human-induced activities, notably agricultural expansion and urbanization processes, emerged as the predominant driving forces during the period from 1980 to 2000. However, while human activities continued to impart their influence, the roles of natural determinants such as precipitation have become progressively more apparent during the period from 2001 to 2018. Importantly, the influences exerted by human activities and natural environmental factors on these wetlands are not standalone; there is a marked interplay between them. This interaction, typically presents a nonlinear amplification among the varied influencing factors. The results of this study provide supportive data and scientific evidence for the ecological restoration and conservation of wetlands. small and micro wetlands changing environment western Jilin Province high-latitude region Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Small and micro wetlands constitute a vital component of the ecosystem mosaic, their significance manifested in the domains of hydrological regulation, carbon cycling, water quality enhancement, and biodiversity preservation (Gxokwe et al. 2022 ). Their rich biodiversity is a testament to the heterogeneity inherent within the wetland environment. An array of ecological niches is formed due to variations in parameters such as water depth, soil type, nutrient conditions, and water quality, creating a diverse biological haven (Ahn 2015 ). Their hydrological function emerges prominently in their capability to absorb, store, and ultimately release water. This capacity allows them to mitigate flood events, stabilize groundwater tables, and maintain consistent water supply during periods of drought (Bullock and Acreman 2003 ). Furthermore, small and micro wetlands operate as key nodes in the global carbon cycle. Through biological carbon fixation, accumulation of organic carbon, and microbial mediation, they serve as effective carbon sinks, thus contributing positively to the alleviation of anthropogenic climate change (Bridgham et al. 2013 ; Bullock and Acreman 2003 ; Craft 2008 ). Another noteworthy attribute is their ability to purify water. They have the capacity to adsorb particulate matter, transform surplus nitrogen and phosphorus, and facilitate the degradation of certain toxins, thereby playing an indispensable role in augmenting water quality and enhancing aquatic ecosystem health (Verhoeven et al. 2006 ; Vymazal 2011 ). In recent years, it has been widely recognized within the scientific community that external factors, including changes in climate and land use, could impose significant effects on the functions and geographical distribution of small and micro wetlands (Pereira et al. 2021 ; Tian et al. 2023 ; Zhang et al. 2023 ). However, our current comprehension of these impacts remains relatively nascent. Thus, it is of utmost importance to conduct further research to predict and evaluate possible future transformations in small and micro wetlands, such as alterations in their ecological functions, spatial coverage, and distribution patterns. The enumeration and spatial demarcation of small and micro wetlands predominantly rely on methodological triptych consisting of terrestrial surveys, the implementation of GIS and remote sensing technologies, and the application of computational models and algorithms. Ground-based surveys proffer direct and unequivocally precise evaluations of the prevalence and spatial extent of these wetlands. However, due to the considerable expenditure of human, physical, and financial resources, this modus operandi is ordinarily restricted to specialized investigations with limited geographical scope (O’Sullivan 2007 ). Contrastingly, models and algorithms offer a more economically efficient alternative, extrapolating the quantity and area of wetlands utilizing established environmental variables and historical datasets. Nonetheless, the precision of such methods is typically contingent on the intricacy of the computational model and the integrity of the input data. For the study of small and micro wetlands, more granular and accurate data may be necessitated. Remote sensing technology represents a powerful asset in the domain of small and micro wetland research, enabling the extraction of geospatial data on an extensive scale, thus facilitating wide-ranging or even global monitoring and investigation. Compared to field surveys, data acquisition through remote sensing is not only cost-effective but also efficient, yielding long-term and consistent time-series data, which is paramount in elucidating dynamic transformations and longitudinal trends (Marechal et al. 2012 ; Zhang et al. 2017 a). Furthermore, the non-invasive nature of remote sensing, coupled with its ability for unattended operation, renders it an indispensable tool for the examination of sensitive or inaccessible wetland territories. Notwithstanding, the limited resolution of remote sensing imagery may pose challenges in capturing the minutiae of smaller wetlands. In the realm of small and micro wetland research, the delimitation of their spatial extent constitutes a critical determinant. Yet, the lack of a universally accepted standard for this definition has led scholars to adopt various classifications and definitions based on their research milieu and objectives. For instance, Euliss et al. characterize small and micro wetlands as those with an area less than 1 hectare(Euliss et al. 2004 ), whereas Downing et al. categorize wetlands smaller than 10 hectares as small wetlands(Downing et al. 2006 ). In certain national or regional contexts, researchers have implemented alternative area segmentation criteria. For instance, Tiner, in a survey of U.S. wetland resources, delineated wetlands into four categories predicated on size, deeming a range of 2–10 acres (0.8-4.0 hectares) as small wetlands(Tiner R.W 2003 ). In the typology of Chinese wetlands, wetlands less than 8 hectares were defined as small and micro wetlands in the \"Guidelines for Strengthening the Conservation and Management of Small Wetlands,\" submitted to the United Nations in 2022 Given the influence of research context and objectives on the definition and demarcation of small and micro wetlands, bespoke adaptations must be made commensurate with the specific circumstances of each investigation. Moreover, both the concept and spatial delineation of small and micro wetlands warrant further exploration and refinement. An array of computational models, inclusive of the Geodetector, PLUS model, SWAT model, LANDIS model, and CA-Markov model, has been pervasively leveraged to elucidate influencing factors of shifts and to prognosticate future modifications in the realm of small and micro wetlands (G Arnold et al. 2012 ; He et al. 2008 ; Scheller and Mladenoff 2007 ; Wang et al. 2010 ).The Geodetector model, proficient in the management of high-dimensional, non-linear, non-monotonic, and complexly interrelated environmental impacts, is exceptional at revealing the heterogeneity of factors driving wetland alterations. Nonetheless, it imposes stringent prerequisites on the quality of spatial data inputs (Xue et al. 2023 ). The PLUS model, distinguished by its emphasis on spatial implications and the incorporation of manifold driving components, exhibits prowess in simulating the dynamism of wetland transitions(Gao et al. 2022 ; Li et al. 2022 ; Qu et al. 2019 ). Conversely, the SWAT model predominantly aims at the simulation of hydrological and nutrient fluxes within a basin-centric paradigm (Zhang et al. 2022); the LANDIS model, functioning as an ecosystem simulator, is equipped to replicate a myriad of ecological processes, including forest succession, wildfires, and pathogen dynamics, and their temporal and spatial implications on forest architecture and species composition, rendering it exceptionally apposite for spatial dynamic simulations within forest ecosystems(Schrum et al. 2020 ; Wu et al. 2022 a).The CA-Markov model, a spatially explicit simulator tailored for land use/cover transitions, amalgamates Cellular Automata (CA) to reflect spatial vicissitudes in land use/cover archetypes, while utilizing the Markov chain to depict temporal oscillations. It operates under the Markovian presumption that alterations in land use/cover adhere to a Markov process, intimating that the future trajectory of land use/cover is singularly predicated on its prevailing state and disassociated from antecedent conditions (Ait El Haj et al. 2023; Fu et al. 2022 ; Tariq et al. 2022 ). Among these computational instruments, the Geodetector exhibits proficiency in effectively recognizing and quantifying the heterogeneity of drivers precipitating small and micro wetland transitions, thereby precisely demystifying the strength and modalities of diverse environmental factors impinging on these transformations. Moreover, the Geodetector exemplifies versatility and pragmatic utility in deciphering the determinants of wetland transitions, rendering it broadly adaptable across an array of wetland systems of disparate types and magnitudes. The Western Jilin Province is located in China’s high latitude northeast, and it is one of important area distributed many wetlands (L et al. 2018). In March of 2022, a significant stride was made when the Chinese government tendered a draft resolution entitled \"Guidelines for the Conservation and Management of Small and Micro Wetlands\" (1). This seminal document served as a catalyst, fostering a broader understanding of the crucial importance of safeguarding these diminutive wetland ecosystems. Despite the establishment of an array of expansive wetland reserves within this domain (Han et al. 2022 ), research focusing on the nuanced dynamics of small and micro wetlands remains noticeably sparse, with a corresponding dearth of foundational data on these ecosystems. In light of this, our proposed research endeavor intends to harness Landsat satellite imagery spanning the period from 1980 to 2018, complemented by Land Use and Land Cover Change (LULC) classification methodologies, to extract and elucidate wetland data within this geographical locale. Capitalizing on the synergistic use of the Geodetector model, we are committed to embarking on an in-depth exploration of the multitude of factors engendering transformations within the small and micro wetlands in this region, as well as conjecturing about potential trajectories of their future evolution. The primary objectives delineated in this research are three-fold: (1) to collate and dissect the spatio-temporal distribution and dynamic shifts experienced by small and micro wetlands in Western Jilin Province over the past 38 years; (2) to unveil the intrinsic correlations interlinking alterations within these wetlands and broader land use transformations; (3) to engage in a quantitative evaluation of the magnitude of impact rendered by climate change and anthropogenic activities on the transmutations within these small and micro wetlands. By undertaking this investigation, we aspire to accrue a profound understanding of the evolutionary mechanisms governing small and micro wetlands under the duress of climate change and human influences. Concurrently, we anticipate that the methodological approach employed within our study could potentially serve as a guiding beacon for analogous research pursuits concerning small and micro wetlands in diverse regions. 2. Materials and Methods 2.1. Study Area The western Jilin Province is situated at the intersection of the Horqin Grassland and Songliao Plain, adjacent to the Great Xing'an Mountains forest area. It includes 11 cities and counties (Fig. 1 ) with a total area of approximately 52,300 square kilometers, accounting for 28.1% of the total area of Jilin Province. The area lies within the second major subsidence belt of the Xinhuaxia Formation, characterized by relatively low terrain and gentle topography, with winding and meandering rivers that have insufficient drainage capacity. The climate in this region is classified as a temperate semi-humid continental monsoon climate, with precipitation concentrated in the summer. The precipitation and evaporation rates are relatively close, which is conducive to the formation and maintenance of wetlands. In recent years, the wetlands in western Jilin Province have faced challenges and changes due to agricultural expansion and climate change. With the expansion of agriculture, the surrounding land of wetlands has been reclaimed for farmland and agricultural activities, resulting in wetland degradation and ecosystem disruption. Furthermore, agricultural expansion leads to the reduction and fragmentation of wetland habitats, affecting the migration and reproduction of wetland organisms. Warming temperatures have intensified water evaporation and altered precipitation patterns, which may affect the supply and stability of wetland water resources. 2.2. Data Source The land use data used in this study were obtained from the Resource and Environment Science Data Center of the Chinese Academy of Sciences ( http://www.resdc.cn/ ). The data have a spatial resolution of 30 meters and were derived by analyzing Landsat satellite images. Through field validation, the classification accuracy of the data reached 91.2%, meeting the data accuracy requirements of this study. The meteorological data were obtained from the China Meteorological Data Service Center ( http://data.cma.cn/ ). The annual average temperature and annual precipitation data were derived from monthly average temperature and monthly precipitation observations from meteorological stations in the northeastern region where the study area is located. The digital elevation model (DEM) data were sourced from the Geographic Spatial Data Cloud Platform ( http://www.gscloud.cn/).Th e GDP data were obtained from the Statistical Yearbook in Jilin Province ( http://tjj.jl.gov.cn/ ). 2.3. Methodology (1) Wetland Dynamic Analysis Based on the land use classification system from the data source and the actual situation in western Jilin Province, the main land use types considered were paddy fields, dry fields, forests, grasslands, Construction land, water bodies, marshland, saline-alkali land, and unused land. Marshland and water bodies were included in the wetland category for western Jilin Province. The \"Extract by Attributes\" tool in ArcGIS was used to extract wetland patches. Depending on the vector or raster data type, a new field was added or the \"Zonal Statistics\" tool was used to calculate the area of each wetland patch. The wetland patches that met the predetermined area range for small-scale wetlands (0.09-8.0 hectares) were selected. Finally, the \"Statistics\" function in the attribute table was used to calculate the number and corresponding area of the selected small-scale wetlands. This entire process involved the use of various tools in ArcGIS to extract and analyze small and micro wetlands. (2) Geodetector Model The Geodetector is a statistical method used to detect spatial heterogeneity and uncover the underlying drivers of the spatial patterns. It aims to evaluate the spatial relationship between independent variables and the dependent variable. The core idea of the Geodetector is based on the assumption that if an independent variable has a significant impact on a dependent variable, their spatial distributions should exhibit similarity. The unique advantage of the Geodetector lies in its ability to identify the interaction effects among factors on the dependent variable. Traditional regression models test the significance of interaction effects by adding interaction terms of two factors. However, the Geodetector assesses the presence, strength, direction, and linearity/non-linearity of interaction effects by calculating and comparing the q-values of individual factors and the q-value of their combination. By employing the Geodetector, researchers can gain insights into the relative contributions of individual factors and their interactions in driving the variations of the dependent variable. This method provides a valuable tool for understanding the complex relationships between spatial variables and can assist in identifying the driving mechanisms behind spatial patterns. In the process of using Geodetectors for correlation analysis, it is necessary to discretize continuous spatial data into grids or zones. Here are the steps involved: ① Determine the appropriate grid size or zoning method based on the size of the study area and data resolution, balancing accuracy and computational complexity. ② Grid the spatial data of each influencing factor, ensuring that the grid cell size matches the selected grid size and using the same projection coordinate system and spatial extent. ③ When aggregating data, assign multiple observation points or samples to their respective grids or zones and calculate statistical indicators such as averages or totals within each zone. By following these steps, suitable discretized spatial data can be prepared for geodetector analysis. This process allows for the exploration of spatial relationships and the identification of driving forces behind the patterns of interest. In this study, the nearest neighbor allocation method is used to resample the indicators. This method is the fastest interpolation method and is suitable for discrete data. It does not change the pixel values. To balance the impact of sample quantity and density on computational efficiency, a sampling grid size of 5 km × 5 km is chosen. Within the study area in western Jilin Province, the influencing factors are uniformly sampled using a 5 km × 5 km grid, resulting in a total of 2081 sampling points. The nearest neighbor allocation method is used to resample the influencing factors at the sampling points, generating raster data. The raster cell values are extracted based on the geometric center of the sampling points and recorded as output features. Then, Geodetectors are used to calculate the impact intensity and interaction effects of the various influencing factors on the changes in small-scale wetlands in western Jilin Province. Two dependent variables are selected in this experiment: the number of small-scale wetlands (Y1) and the area of small and micro wetlands (Y2). The independent variables include paddy fields (X1), dry fields (X2), construction land (X3), saline-alkali land (X4), evaporation (X5), precipitation (X6), aridity index (X7), and GDP (X8). The q-value is calculated to measure the explanatory power of the independent variables (Xi) on the dependent variable (Y). 3. Results and Analysis 3.1. Spatiotemporal changes in small and micro wetlands (1) The temporal evolution of small and micro wetlands Through comparative analysis of the extracted small and micro wetlands from 1980 to 2018 (Fig. 2 ), it was found that the number and area of small and micro wetlands showed a decreasing trend. From 1980 to 1990, the number and area of small and micro wetlands sharply decreased by 83.1% and 54.0%, respectively. From 1990 to 2010, although the decrease was relatively small, it still showed a downward trend, with a decrease of 29.3% in quantity and 36.5% in area. During the period from 2010 to 2018, although the number of small and micro wetlands rebounded, the area continued to decrease, increasing by 8.7% and decreasing by 4.8%, respectively. Except for Fuyu City, all regions have experienced a significant reduction in the number and area of small and micro wetlands. The degradation of small and micro wetlands in the Da'an City is particularly evident in terms of quantity, with a decrease of 1107 wetlands from 1139 in 1980 to 32 in 2018. In terms of area, the wetland area decreased from 722.20 square kilometers in 1980 to 126.34 square kilometers in 2018, a decrease of 595.86 square kilometers. While wetlands degrade, the average wetland area increases. Compared to larger wetlands, smaller wetlands have a higher risk of loss. The number and area of small and micro wetlands in Fuyu City showed a fluctuating and increasing trend throughout the observation period. From 1980 to 2018, the number of small and micro wetlands in Fuyu City increased by 36, with a growth rate of 42.9%. At the same time, the total area of wetlands has also increased by 28.2 square kilometers, with a growth rate of 29.9%. This may be related to the construction of natural reserves such as the Southwest River Beach Wetland in Fuyu City, which provides a suitable environment and resources for protecting and restoring wetland ecosystems. (2) Spatial evolution of small and micro wetlands From the spatial change graph of small and micro wetlands (Fig. 3 ), it can be seen that in the past decades, some areas in western Jilin Province have experienced a decrease in small and micro wetlands. For example, in Da'an City and Qian'an County in the central region, especially in the surrounding cities and farmland areas, many wetlands have been affected by urbanization and agricultural expansion, resulting in reduced or completely disappeared areas. The number and area of wetlands in these areas are showing a downward trend. On the other hand, some regions have seen new additions in the distribution of small and micro wetlands in the Taonan City. For example, the northeastern border area, located in the Songhua River basin, may be affected by water resource management, ecological restoration and protection plans. New small and micro wetlands may be formed through natural succession or artificial restoration measures, providing new wetland habitats for local ecosystems. In the western Jilin Province, there are still some areas where the distribution of small and micro wetlands is relatively stable, that is, the number and area of wetlands have remained basically unchanged in the past few decades. These areas may be affected by better protection measures or natural conditions, allowing wetlands to exist relatively stably ecological functions. 3.2. Analysis of driving factors for the evolution of small and micro wetlands (1) Analysis of the explanatory power of a single factor Between 1980 and 1990, the three factors of construction land (0.8693), drought index (0.7853), and saline-alkali land (0.7782) had high explanatory power, indicating that they contributed significantly to the changes in the number of small and micro wetlands during this period. The explanatory power of precipitation (0.6566), GDP (0.5798), evaporation (0.5302), dry fields (0.3457), and paddy fields (0.2382) is relatively low. During this period, with the acceleration of urbanization and the promotion of economic development in the western Jilin Province, a large number of natural wetlands were converted into construction land, resulting in a sharp decrease in the area of small and micro wetlands. The expansion of construction land has led to a transformation in land use, with wetlands being landfilled, reclaimed, and developed to meet the needs of urban expansion and population growth. Between 1990 and 2000, construction land (0.8142), saline-alkali land (0.7067), and precipitation (0.5956) had a high explanatory power. However, the explanatory power of paddy fields (0.4045), GDP (0.2468), evaporation (0.2121), dry fields (0.1453), and drought index (0.1206) is relatively low. Similar to the 1980s, human activities such as the expansion of construction land still have a high contribution to the loss of small and micro wetlands. Between 2000 and 2010, precipitation (0.7353), construction land (0.7227), and saline-alkali land (0.6452) had high explanatory power. The explanatory power of evaporation (0.4790), drought index (0.3039), dry field (0.2927), GDP (0.1655), and paddy field (0.1023) is relatively low. During this period, the decrease in precipitation led to a decrease in water resources and a decrease in water levels in western Jilin Province, which in turn affected the hydrological conditions and water supply of small and micro wetlands. Due to the high dependence of small and micro wetlands on water to maintain their humid ecological environment, the reduction of precipitation may have a certain promoting effect on the degradation of small and micro wetlands. Between 2010 and 2018, precipitation (0.9142), saline-alkali land (0.8500), and GDP (0.5794) had high explanatory power. The explanatory power of construction land (0.5295), evaporation (0.4933), drought index (0.4624), paddy field (0.2214), and dry field (0.1617) is relatively low. Similar to the 2000s and 2010s, natural factors such as increased precipitation still have a high contribution to the loss of small and micro wetlands. During this period, the increase in precipitation provided more water supply for the wetland, improving its hydrological conditions and water level. This increased precipitation helps to increase the water area and volume of wetlands, providing more habitats and survival resources, promoting the prosperity of wetland vegetation and increasing biodiversity. Overall, the main factors that affect the changes in the area of small and micro wetlands and their explanatory power vary at different time periods. Throughout all time periods, construction land, saline-alkali land, and precipitation have high explanatory power, indicating that these factors contribute significantly to the changes in the area of small and micro wetlands Between 1980 and 1990, construction land (0.8272), saline-alkali land (0.7679), and precipitation (0.7190) had high explanatory power, indicating that they contributed significantly to the changes in the area of small and micro wetlands during this period. The explanatory power of evaporation (0.6413), drought index (0.6317), GDP (0.4808), dry fields (0.2792), and paddy fields (0.1645) is relatively low. The 1980s was the peak period of infrastructure construction in the western Jilin Province, including the construction of roads, railways, water conservancy facilities, etc. These infrastructure constructions require a large area of land, including wetland areas. Wetlands used as fill materials for infrastructure construction or directly covered can have adverse effects on the protection of small and micro wetlands. Between 1990 and 2000, precipitation (0.8602), saline-alkali land (0.8291), and construction land (0.7751) had high explanatory power. The explanatory power of GDP (0.4834), evaporation (0.3225), drought index (0.2493), dry fields (0.2457), and paddy fields (0.0793) is relatively low. In the 1990s, the western Jilin Province experienced a decrease in precipitation. Less precipitation leads to a decrease in the supply of surface water and groundwater, resulting in insufficient water supply in small and micro wetlands. Wetland ecosystems have a high dependence on water, and a lack of sufficient water supply will lead to wetland drought and vegetation degradation, thereby promoting wetland degradation. Between 2000 and 2010, saline-alkali land (0.8787), evaporation (0.6921), and GDP (0.6687) had high explanatory power. The explanatory power of dry fields (0.6323), paddy fields (0.4751), drought index (0.3523), construction land (0.3278), and precipitation(0.2935) is relatively low. During this period, the expansion of saline-alkali land and soil degradation have become important reasons for the reduction of small and micro wetlands. The salinization of land leads to high salt concentration in the soil around wetlands, which in turn affects the water quality and vegetation growth of wetlands. The expansion of saline-alkali land not only reduces the area of wetlands, but also leads to the degradation of wetland ecosystems, reduction of plant species, and deterioration of water quality. Between 2010 and 2018, saline-alkali land (0.8843), drought index (0.6865), and precipitation (0.6850) had high explanatory power. The explanatory power of GDP (0.6559), evaporation (0.6320), paddy fields (0.5052), construction land (0.3177), and dry fields (0.2133) is relatively low. During this period, the reduction in the area of small and micro wetlands has decreased, with the main contributing factor still being natural factors such as saline-alkali land. Overall, the influencing factors vary across different time periods, with natural environmental factors (such as precipitation, saline-alkali land, evaporation, and drought index) and human activities (such as GDP, dry fields, paddy fields, and construction land) having a high explanatory power on the changes in the quantity of small and micro wetlands. At different time periods, the explanatory power of certain factors may significantly increase or decrease, which may be related to comprehensive factors such as climate change, land use change, and economic development at that time. (2) Multifactor interaction analysis The driving factor only exhibits bilinear and nonlinear enhancement. This indicates that the interaction between any two factors, the 8 selected in this study, is greater than any single factor and does not weaken the effect. These results indicate that the comprehensive effect of various factors is stronger in explaining changes in small and micro wetlands than a single factor, and has a more positive driving effect on changes in small and micro wetlands. Between 1980 and 1990, there was a high correlation between factors such as construction land (X3), saline-alkali land (X4), evaporation (X5), precipitation (X6), and drought index (X7), which may collectively affect the reduction of the number of small and micro wetlands. The impact of economic development (X8) on the changes in the number of small and micro wetlands is relatively small. In the analysis of changes in the number of small and micro wetlands between 1990 and 2000, it was mainly found that factors such as construction land (X3), saline-alkali land (X4), and evaporation (X5) have a high correlation with other factors, especially with dry fields (X2) and precipitation (X6). This indicates that these factors may collectively affect the reduction of the number of small and micro wetlands during this period. In addition, the correlation between precipitation (X6) and other factors is relatively high, further indicating the impact of reduced precipitation during this period on the reduction of the number of small and micro wetlands. The correlation between GDP (X8) and other factors is relatively low, which may indicate that during this period, the impact of economic development on the changes in the number of small and micro wetlands is relatively small. In the analysis of the changes in the number of small and micro wetlands between 2010 and 2018, we observed a generally high correlation between precipitation (X6) and other factors, especially with paddy fields (X1), dry fields (X2), and evaporation (X5). This indicates that precipitation has a significant impact on the changes in the number of small and micro wetlands during this period. The correlation between saline-alkali land (X4) and other factors is also high, emphasizing once again the impact of intensified land salinization on the reduction of the number of small and micro wetlands. In addition, the two meteorological factors of evaporation (X5) and drought index (X7) remained highly correlated with other factors during this period, further highlighting the key impact of meteorological conditions on the changes in the number of small and micro wetlands. At the same time, the correlation between GDP (X8) and other factors has increased, especially with dry fields (X2), which may indicate that economic development has a more significant impact on the changes in the number of small and micro wetlands during this period. Between 1980 and 1990, the impact of paddy fields (X1) on the changes in the area of small and micro wetlands was relatively small, and the interaction with other factors was weak. The interaction between dry land (X2) and factors such as construction land (X3), saline-alkali land (X4), and evaporation (X5) is strong, indicating that these factors collectively affect the reduction of small and micro wetland area. In addition, precipitation (X6), drought index (X7), and GDP (X8) also have a high interaction with other factors, indicating that these factors reduce the area of small and micro wetlands. During the period from 1990 to 2000, the analysis of multi factor interaction data from geographic detectors showed that the interaction between paddy fields (X1) and other factors was weak, and the impact on the changes in the area of small and micro wetlands was relatively small. The strong interaction between factors such as dry land (X2), construction land (X3), and saline-alkali land (X4) indicates that these factors collectively affect the changes in the area of small and micro wetlands. At the same time, precipitation (X6) has a strong interaction with other factors during this period, especially with building land (X3) and saline-alkali land (X4). The interaction between evaporation (X5) and other factors is relatively weak, but there is still a certain connection with some factors. In addition, the drought index (X7) and GDP (X8) also had a high interaction with other factors during this period, indicating that they played a role in the changes in the area of small and micro wetlands. During the period from 2000 to 2010, the analysis of multi factor interaction data from geographic detectors showed that the interaction between paddy fields (X1) and other factors was weak, and the impact on the changes in the area of small and micro wetlands was relatively small. The interaction between dry land (X2) and construction land (X3) is very strong, indicating that these factors collectively affect the changes in the area of small and micro wetlands. In addition, the interaction between dry land (X2) and other factors is also significant. The strong interaction between factors such as construction land (X3), saline-alkali land (X4), and precipitation(X6) indicates that they collectively affect the changes in the area of small and micro wetlands. The interaction between evaporation (X5) and other factors is relatively weak, but there is still a certain connection with some factors. Precipitation (X6) has a strong interaction with other factors during this period, especially with building land (X3) and drought index (X7). In addition, during this period, the interaction between drought index (X7) and other factors was also high, indicating that they played an important role in the changes in the area of small and micro wetlands. The interaction between GDP (X8) and other factors is relatively weak, but there is a certain degree of interaction with factors such as precipitation (X6) and drought index (X7). During the period from 2010 to 2018, multi factor interaction analysis data from geographic detectors showed that the interaction between paddy fields (X1) and other factors was still weak, and the impact on changes in the area of small and micro wetlands was relatively small. The interaction between dry land (X2) and construction land (X3) is very strong, and these factors collectively affect the changes in the area of small and micro wetlands. Meanwhile, the interaction between dry land (X2) and other factors is also significant. The interaction between construction land (X3) and factors such as saline-alkali land (X4) and precipitation (X6) remains strong during this period. 4. Discussion Microscopic and diminutive wetlands are instrumental in the sustenance of ecosystem functions and the preservation of biodiversity. However, the micro-wetlands in the western Jilin Province have exhibited an unmistakable trajectory of degradation over the preceding four decades. It has been quantified that the spatial extent of wetlands in the northeastern region has contracted by over 20% in the past two decades, coupled with a decline in the environmental quality of these wetlands. The primary factors contributing to wetland degradation include natural catastrophes such as droughts and floods, alongside anthropogenic influences encompassing land development, environmental contamination, and overutilization (Cui et al. 2018 ; Pal and Talukdar 2018 ; Wang et al. 2012 ; Zhang et al. 2010 ).Research conducted by Li et al. on the wetland ecosystems in the northeastern region illuminated a dire issue of significant biodiversity reduction within these ecosystems, with habitat loss triggered by wetland degradation implicated in the endangered status of numerous pivotal species(Zhang et al. 2021 ) Analysis by Zhang et al. of the sensitivity of the wetland ecosystems in western Jilin Province emphasized a heightened degree of sensitivity, and proffered strategies for the reversion of cultivated lands to grasslands and wetlands(L et al. 2016 ).In the course of the past several decades, the micro-wetlands of western Jilin Province have undergone profound transformations in landscape patterns and sustained considerable degradation. The extent of these wetlands has dwindled from 2801.68 km 2 in 1980 to a mere 818.28 km 2 in 2018, and the number has precipitously plunged from 2604 to 337 during the same period. When scrutinized from the perspective of landscape ecology, these alterations manifest intricate spatio-temporal dynamics. Notably, the distribution of these wetlands has gradually diversified from a concentrated pattern, thereby indicating pronounced spatial heterogeneity. The degradation of micro-wetlands in the Da'an and Qian Gorlos regions has been especially conspicuous. In contrast, the fluctuation in the area and quantity of micro-wetlands in southern regions, such as Fuyu, has been comparatively nominal.As depicted in Fig. 8 , urban construction land in western Jilin Province has been on a steady expansionary course since 1980. This trend is potentially interrelated with the surge in local industrial development and the progression of urbanization. Despite the overall shrinkage in wetland coverage, the degradation of micro-wetlands has been particularly stark. Through a meticulous analysis of the patterns of land utilization with in western Jilin Province (depicted in Fig. 8 ), it becomes evident that the timespan from 1980 to 2018 has witnessed a perpetual augmentation in construction land and paddy fields, contrasted with a ceaseless diminution in grassland areas. Interestingly, wetlands initially exhibited a proliferation, subsequently superseded by a contraction, intimating a significant metamorphosis in the overall landscape configuration (as portrayed in Fig. 8 ). The insights derived from the analysis are as follows: The paddy fields' landscape has experienced the most pronounced expansion, accruing a cumulative increase of 2885.49 km² over the 38 years. The period from 2010 to 2018 is noteworthy due to its substantial expansion-approximately twentyfold relative to the preceding decade. During this time, the spatial extent of paddy fields amplified by 3.53%. The county-level administrative divisions registering the most substantial growth in paddy fields over the 38 years encompass Qian Gorlos (1442.75 km²), Zhenlai (610.27 km²), and Songyuan (497.61 km²), collectively accounting for 88.39% of the total paddy field expansion in western Jilin Province. These regions, characterized by lower terrain, geographical remoteness, and economic underdevelopment, have undergone significant paddy field expansion. It is pertinent to note that Taonan is the sole region in western Jilin Province where paddy fields demonstrated a trend of negative growth (-0.26%). This phenomenon can be attributed to the convergence of certain factors such as geographic isolation, the flatness of terrain facilitating paddy field development, and the local government's aggressive encouragement of agricultural production and substantial agricultural support. Grasslands have experienced the most extensive contraction, with a net decrease of 3977.96 km² over the 38 years. The regions of Tongyu County and Qian Gorlos County have been subjected to relatively significant contractions, while Fuyu County and Songyuan City have encountered smaller diminutions. Wetlands have undergone a cycle of initial proliferation followed by a subsequent decline. The increment in the first decade stood at 8.5%, whilst the ensuing decrement rate was 34.08%, culminating in a net decrease of 1076.32 km². The regions with the largest scale of contraction are predominantly localized in Fuyu County and Da'an City. When considering the potential impact of human activities: there is a conspicuous expansion of artificially exploited land. In terms of natural ecological land, although forested land area has witnessed some proliferation, the overarching trend is one of significant contraction. A palpable correlative synergy exists between the shrinkage of natural ecological land and the expansion of agricultural and construction land. When scrutinizing the historical trajectory of petite and microscopic wetlands with in western Jilin Province, a distinct dichotomous trend emerges. The initial stage spanning from 1980 to 2000 marked a pronounced contraction in both the quantity and geographic span of these wetlands. Simultaneously, we noted a surge in developed land, emerging as a prominent variable. During this phase, urbanization-induced land fragmentation (Liu et al. 2017 ), ecological degradation (Li et al. 2008), and industrial-agricultural activities (L et al. 2016) were paramount contributors. Notably, areas undergoing rapid urbanization, such as Da'an City and Tongyu County, were subjected to more severe degradation of their wetland ecosystems, an assertion that aligns with prior studies (Birch et al. 2022 ; Fei et al. 2016 ; Lin et al. 2022 ; Mao et al. 2018 ; Xiong et al. 2023 ).Transitioning into the subsequent period from 2000 to 2018, climatic elements assumed a significant role in sculpting the transformations of western Jilin Province’s petite and microscopic wetlands. The primary catalysts in this phase were increases in precipitation and temperature. Some reachers (Sun et al. 2022 ) reinforced the idea that shifts in precipitation patterns, propelled by global warming and concurrent temperature increases, imposed substantial disruptions on the wetlands' hydrological balance. These dynamics triggered fluctuations in wetland humidity and accelerated evaporation rates, thereby destabilizing the ecosystem (Fay et al. 2016 ; Werner et al. 2013 ; Zhang et al. 2017 b).Intriguingly, between 2010 and 2018, a paradoxical trend emerged. Despite a consistent rise in precipitation and temperature in western Jilin Province, there was a surprising increase in the number of petite and microscopic wetlands, while their geographic spread diminished. This counterintuitive phenomenon was clarified by the works of Li et al. (Li and Shi 2015 ) and Wu et al. (Wu et al. 2022 b). They proposed that intensified precipitation predominantly augments the wetland's water supply. In the short term, increased precipitation directly inflates the wetlands' hydrological content through enhanced water influx, thereby proliferating their numbers (Mohamed and Savenije 2014 ; Xue et al. 2018 ). However, persistent precipitation could induce a state of hydrological oversaturation within wetlands, provoking a contraction in their spatial extent. This scenario could be predicated on reduced soil solubility in the wetlands, potentially leading to salinization(Gell et al. 2007 ; Jolly et al. 2008 ; Zheng et al. 2022 ).The results are consistent with those from geographical detectors indicating a high contribution of saline-alkali land. Understanding the spatiotemporal dynamics of petite and microscopic wetlands necessitates an appreciation of the intricate nexus of multiple influencing factors (Chabudzinski et al. 2018 ; Liu et al. 2021 ; Rouissi et al. 2014 ). Between 1980 to 2000, the dynamics of these wetlands in western Jilin Province were significantly shaped by an upsurge in urbanization and agricultural activities, such as the expansion of farmland and rapid urban development, that epitomized human interventions. As we crossed into the 21st century, the dominance began shifting towards natural elements, particularly climate change and alterations in precipitation patterns, although the influence of human activities persisted. Rather intriguingly, they demonstrated a nonlinear amplifying trend in synergy with the natural factors, together driving changes in these diminutive wetlands. Since 2010, the western Jilin Province has been progressively implementing the Interconneted River System Network projects aimed at enhancing river and lake connectivity, intending to bolster the resilience of extensive wetlands to climate change and anthropogenic disturbances (Suir and Sasser 2019 ). However, the project ignores the special characteristics of small and micro wetlands and fails to meet their water supply needs (Yang et al. 2016 ). The confluence of land use practices, climatic conditions, and economic development models further complicates this landscape, creating a complex network of influences shaping these petite and microscopic wetlands. Economic development-driven transitions in land use, such as urbanization and the expansion of agricultural lands, can potentially lead to encroachment upon wetlands (Peng et al. 2021 ; Wang et al. 2020 ), and concurrently, these human-induced land use changes can directly impact the hydrological and ecological functionality of wetlands (Rafiei et al. 2022 ; Volik et al. 2020 ). Climate change, manifesting as shifts in precipitation patterns and temperature, can impact water resources, instigating further alterations in the wetland environment (Herrera-Pantoja et al. 2012 ; O'Keeffe et al. 2019 ; Wilson et al. 2022 ). The repercussions extend to influencing agricultural yields, subsequently affecting land use patterns and economic trajectories. Additionally, climate change-induced adaptive economic investments, such as the development of irrigation infrastructure, may reciprocally impact the economic development models (Malek et al. 2018 ; Rolim et al. 2017 ). The collective and cumulative impact of these interplaying factors can potentially induce a holistic transformation in petite and microscopic wetlands. Consequently, the conservation strategies for these wetlands should adopt an integrative approach, acknowledging and incorporating these intertwined relationships, to develop holistic plans that can both accommodate the challenges of climate change and ensure the preservation of these critical ecosystems. 5. Conclusions Based on remote sensing data and Geodetector Model from 1980 to 2018, the impact of natural factors and human activities on small and micro wetlands in a high latitude region was quantitatively analyzed. The main conclusions are as follows: (1) The small and micro wetlands in the high latitude region have suffered serious degradation due to climate change and human activities. This trend, while endemic across the in the high latitude region's wetlands, is notably more pronounced within these specific small and micro wetlands in the Western Jilin Province. The spatial area and number of small and micro wetlands decreased by 70.8% and 87.1% in the Western Jilin Province, respectively. (2) Impacts of driving factors on small and micro wetlands are characterized by divergence at different periods. In the period from 1980 to 2000, anthropogenic factors exerted a considerable influence, resulting in significant declines in both the quantity and spatial extent of these wetlands. Conversely, the period from 2000 to 2018 witnessed a more dominant contribution from climatic factors, which seemingly decelerated the degradation rate of the small and micro wetlands and even stimulated a certain degree of recovery in their numbers. (3) Due to their inherently small scale and dispersed nature, small and micro wetlands are particularly vulnerable to the impacts of both human activities and climatic changes. The inherent risk is that compared to their larger wetlands, the unique requirements of these smaller wetlands are often overlooked. Therefore, it becomes imperative to devise and implement conservation initiatives specifically tailored towards preserving these small and micro wetlands. Declarations Conflicts Interest: The authors have no relevant financial or non-financial interests to disclose. Acknowledgments We are grateful to the staff at Heilongjiang University and the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, for their support and assistance throughout the research process. In addition, we would like to express our gratitude to both the editors and reviewers for their efforts and suggestions. Author Contributions Y.B.W., J.X.S, Y.M.W. and P.Q. conceived the idea of the study and wrote the manuscript; Y.B.W., P.Q. and Y.W carried out data collection and analysis; P.Q., Y.W., G.X.Z. and C.L.D contributed valuable analysis and manuscript review; all authors approved the final manuscript. Funding This research was supported by Outstanding Young Scientist Project in Jilin Province (20230508099RC), National Natural Science Foundation of China (42371037 and 42371169), Major Science and Technology Projects in Jilin Province（20230303007SF）and National Key Research and Development Program of China (2022YFF1300902). 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Water 13:3473. https://doi.org/10.3390/w13233473 Zhang Z, Xue Z, Lu X, et al (2017) Warming in Spring and Summer Lessens Carbon Accumulation over the Past Century in Temperate Wetlands of Northeast China. Wetlands 37:829-836. https://doi.org/10.1007/s13157-017-0915-3 Zheng W, Yang Z, Wang X, et al (2022) Impacts of evaporation and inundation on near-surface salinity at a coastal wetland park. Marine Pollution Bulletin 185:114373. https://doi.org/10.1016/J.MARPOLBUL.2022.114373 Cite Share Download PDF Status: Published Journal Publication published 04 Dec, 2024 Read the published version in Wetlands → Version 1 posted Reviewers agreed at journal 11 Mar, 2024 Reviewers invited by journal 08 Mar, 2024 Editor invited by journal 07 Mar, 2024 Editor assigned by journal 05 Mar, 2024 First submitted to journal 04 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4003007\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":277306580,\"identity\":\"56ac7cb9-562b-46b2-929a-df4e99ed1029\",\"order_by\":0,\"name\":\"Yingbin Wang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACNvb+h4//GNjUs/E3H3yQUFFDWAsfzxlmA56CtAR+iWPJBg/OHCOsRU4ih02C58OhBMmGHDPJhy3MRDiM5+wxCQmDA3kGB86YVSQ2sDHwt3cnEPBLX7KFgcGdYoPDbWU3EnfIMEicObuBgC0HDG8kGDxj3HDg8LYbiWfYGAwkcglokUgwkDhgcBioJcGsILGNmRgtOUaSDQaHE2c2pJgxEKeF51iyMYNBmjEokCUSzhzjIegX+fbmg48Z/tjIgaLy44+KGjn+9l78WjAAD2nKR8EoGAWjYBRgBQBssk7swEs8FQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Heilongjiang University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Yingbin\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":277306581,\"identity\":\"8e3b97eb-8b50-4ad6-b38c-f0419ca3202e\",\"order_by\":1,\"name\":\"Jiaxin Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Northeast Institute of Geography and Agroecology Chinese Academy of Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiaxin\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"},{\"id\":277306582,\"identity\":\"bc97c6eb-8053-43f9-8b94-19937750240e\",\"order_by\":2,\"name\":\"Yao Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Northeast Institute of Geography and Agroecology Chinese Academy of Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yao\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":277306583,\"identity\":\"93a3cb92-82a7-40eb-ac32-4cf8ee4c6641\",\"order_by\":3,\"name\":\"Peng Qi\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0001-7600-2545\",\"institution\":\"Northeast Institute of Geography and Agroecology Chinese Academy of Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Peng\",\"middleName\":\"\",\"lastName\":\"Qi\",\"suffix\":\"\"},{\"id\":277306584,\"identity\":\"ba6b4767-4b7e-4e60-9ca2-8e00d37ed0a8\",\"order_by\":4,\"name\":\"Wenguang Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Northeast Institute of Geography and Agroecology Chinese Academy of Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Wenguang\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":277306585,\"identity\":\"c142ab23-dc73-4ed9-afc8-fccbdcb08d76\",\"order_by\":5,\"name\":\"Yongming Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Heilongjiang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yongming\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":277306586,\"identity\":\"ca919843-c068-4850-8782-a7ff15bcba6b\",\"order_by\":6,\"name\":\"Changlei Dai\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Heilongjiang University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Changlei\",\"middleName\":\"\",\"lastName\":\"Dai\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-03-01 11:18:04\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4003007/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4003007/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s13157-024-01882-9\",\"type\":\"published\",\"date\":\"2024-12-04T15:57:41+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":52499025,\"identity\":\"eba0db70-670c-491c-b448-88a880bc0596\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 09:17:40\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":107308,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eLocation of the Western Jilin Province\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4003007/v1/22b9cdaf33504b1d3d576225.jpg\"},{\"id\":52498884,\"identity\":\"c2bfabc6-a1ec-48d4-a88e-974bf8c1024e\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 09:09:40\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":126230,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eChanges in the number and area of small and micro wetlands in each county (or city) during 1980-2018\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4003007/v1/2067a808d7f3c90d2baca178.jpg\"},{\"id\":52498886,\"identity\":\"b9b25d96-0fe5-4e53-8b1a-61184ab026f7\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 09:09:40\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":104684,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eChanges in spatial distribution of small and micro wetlands during 1980-2018\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4003007/v1/c6fc1d63cdbf5112cf01dbad.jpg\"},{\"id\":52498887,\"identity\":\"c32d61fa-0f8d-49ea-8cd4-d4ba802afbaa\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 09:09:40\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":66313,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe q value of the driving factor for the spatial differentiation of the area of small and micro wetlands in western Jilin Province during 1980-2018\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4003007/v1/22e4783f6f3ebe513741b842.jpg\"},{\"id\":52498888,\"identity\":\"61fbf8fc-a66b-40e2-ad4f-106df52eca0d\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 09:09:40\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":68719,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe q value of the driving factor for spatial differentiation of small and micro wetlands number changes in western Jilin Province during 1980-2018\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4003007/v1/3fe82ad027875c85c285f8d2.jpg\"},{\"id\":52499026,\"identity\":\"d520fd1e-c134-4005-8848-4246d3da762f\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 09:17:40\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":127808,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eContribution of interaction between various factors for the number of small and micro wetland in western Jilin Province during 1980-2018: (a) 1980-1990, (b) 1990-2000, (c) 2000-2010, (d) 2010-2018. (Note: Y1 represents the impacted factor of changes in the number of small and micro wetlands)\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4003007/v1/eb5b024efb0e3cb6086e41a9.jpg\"},{\"id\":52498890,\"identity\":\"d0900500-709d-4be3-93f8-73ba5887749e\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 09:09:40\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":132576,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eContribution of interaction between various factors for area change of small and micro wetland in western Jilin Province during 1980-2018: (a) 1980-1990, (b) 1990-2000, (c) 2000-2010, (d) 2010-2018. (Note: Y2 represents the impacted factor of changes in the area of small and micro wetlands)\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4003007/v1/86b50d247604071d633ccb66.jpg\"},{\"id\":52498891,\"identity\":\"7aedcfa3-9b04-4c23-b276-5357a0244d83\",\"added_by\":\"auto\",\"created_at\":\"2024-03-12 09:09:40\",\"extension\":\"jpg\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":112288,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eLand use change in the western Jilin Province during 1980-2018\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4003007/v1/b7d3cfe094945fc73b4aacae.jpg\"},{\"id\":70965303,\"identity\":\"5df64ac7-2a6f-4dd0-b0f6-c95a6c1176c3\",\"added_by\":\"auto\",\"created_at\":\"2024-12-09 16:18:51\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1537930,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4003007/v1/e054420c-3649-4fdb-8d81-a3ea98acd942.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Spatiotemporal Variation of Small and Micro Wetlands and Their Multiple Responses to Driving Factors in the high-latitude region\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eSmall and micro wetlands constitute a vital component of the ecosystem mosaic, their significance manifested in the domains of hydrological regulation, carbon cycling, water quality enhancement, and biodiversity preservation (Gxokwe et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Their rich biodiversity is a testament to the heterogeneity inherent within the wetland environment. An array of ecological niches is formed due to variations in parameters such as water depth, soil type, nutrient conditions, and water quality, creating a diverse biological haven (Ahn \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Their hydrological function emerges prominently in their capability to absorb, store, and ultimately release water. This capacity allows them to mitigate flood events, stabilize groundwater tables, and maintain consistent water supply during periods of drought (Bullock and Acreman \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). Furthermore, small and micro wetlands operate as key nodes in the global carbon cycle. Through biological carbon fixation, accumulation of organic carbon, and microbial mediation, they serve as effective carbon sinks, thus contributing positively to the alleviation of anthropogenic climate change (Bridgham et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Bullock and Acreman \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Craft \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). Another noteworthy attribute is their ability to purify water. They have the capacity to adsorb particulate matter, transform surplus nitrogen and phosphorus, and facilitate the degradation of certain toxins, thereby playing an indispensable role in augmenting water quality and enhancing aquatic ecosystem health (Verhoeven et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Vymazal \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). In recent years, it has been widely recognized within the scientific community that external factors, including changes in climate and land use, could impose significant effects on the functions and geographical distribution of small and micro wetlands (Pereira et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Tian et al. \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). However, our current comprehension of these impacts remains relatively nascent. Thus, it is of utmost importance to conduct further research to predict and evaluate possible future transformations in small and micro wetlands, such as alterations in their ecological functions, spatial coverage, and distribution patterns.\\u003c/p\\u003e \\u003cp\\u003eThe enumeration and spatial demarcation of small and micro wetlands predominantly rely on methodological triptych consisting of terrestrial surveys, the implementation of GIS and remote sensing technologies, and the application of computational models and algorithms. Ground-based surveys proffer direct and unequivocally precise evaluations of the prevalence and spatial extent of these wetlands. However, due to the considerable expenditure of human, physical, and financial resources, this modus operandi is ordinarily restricted to specialized investigations with limited geographical scope (O\\u0026rsquo;Sullivan \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). Contrastingly, models and algorithms offer a more economically efficient alternative, extrapolating the quantity and area of wetlands utilizing established environmental variables and historical datasets. Nonetheless, the precision of such methods is typically contingent on the intricacy of the computational model and the integrity of the input data. For the study of small and micro wetlands, more granular and accurate data may be necessitated. Remote sensing technology represents a powerful asset in the domain of small and micro wetland research, enabling the extraction of geospatial data on an extensive scale, thus facilitating wide-ranging or even global monitoring and investigation. Compared to field surveys, data acquisition through remote sensing is not only cost-effective but also efficient, yielding long-term and consistent time-series data, which is paramount in elucidating dynamic transformations and longitudinal trends (Marechal et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003ea). Furthermore, the non-invasive nature of remote sensing, coupled with its ability for unattended operation, renders it an indispensable tool for the examination of sensitive or inaccessible wetland territories. Notwithstanding, the limited resolution of remote sensing imagery may pose challenges in capturing the minutiae of smaller wetlands. In the realm of small and micro wetland research, the delimitation of their spatial extent constitutes a critical determinant. Yet, the lack of a universally accepted standard for this definition has led scholars to adopt various classifications and definitions based on their research milieu and objectives. For instance, Euliss et al. characterize small and micro wetlands as those with an area less than 1 hectare(Euliss et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e), whereas Downing et al. categorize wetlands smaller than 10 hectares as small wetlands(Downing et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). In certain national or regional contexts, researchers have implemented alternative area segmentation criteria. For instance, Tiner, in a survey of U.S. wetland resources, delineated wetlands into four categories predicated on size, deeming a range of 2\\u0026ndash;10 acres (0.8-4.0 hectares) as small wetlands(Tiner R.W \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e2003\\u003c/span\\u003e). In the typology of Chinese wetlands, wetlands less than 8 hectares were defined as small and micro wetlands in the \\\"Guidelines for Strengthening the Conservation and Management of Small Wetlands,\\\" submitted to the United Nations in 2022 Given the influence of research context and objectives on the definition and demarcation of small and micro wetlands, bespoke adaptations must be made commensurate with the specific circumstances of each investigation. Moreover, both the concept and spatial delineation of small and micro wetlands warrant further exploration and refinement.\\u003c/p\\u003e \\u003cp\\u003eAn array of computational models, inclusive of the Geodetector, PLUS model, SWAT model, LANDIS model, and CA-Markov model, has been pervasively leveraged to elucidate influencing factors of shifts and to prognosticate future modifications in the realm of small and micro wetlands (G Arnold et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; He et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Scheller and Mladenoff \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e).The Geodetector model, proficient in the management of high-dimensional, non-linear, non-monotonic, and complexly interrelated environmental impacts, is exceptional at revealing the heterogeneity of factors driving wetland alterations. Nonetheless, it imposes stringent prerequisites on the quality of spatial data inputs (Xue et al. \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The PLUS model, distinguished by its emphasis on spatial implications and the incorporation of manifold driving components, exhibits prowess in simulating the dynamism of wetland transitions(Gao et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Li et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Qu et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Conversely, the SWAT model predominantly aims at the simulation of hydrological and nutrient fluxes within a basin-centric paradigm (Zhang et al. 2022); the LANDIS model, functioning as an ecosystem simulator, is equipped to replicate a myriad of ecological processes, including forest succession, wildfires, and pathogen dynamics, and their temporal and spatial implications on forest architecture and species composition, rendering it exceptionally apposite for spatial dynamic simulations within forest ecosystems(Schrum et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Wu et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003ea).The CA-Markov model, a spatially explicit simulator tailored for land use/cover transitions, amalgamates Cellular Automata (CA) to reflect spatial vicissitudes in land use/cover archetypes, while utilizing the Markov chain to depict temporal oscillations. It operates under the Markovian presumption that alterations in land use/cover adhere to a Markov process, intimating that the future trajectory of land use/cover is singularly predicated on its prevailing state and disassociated from antecedent conditions (Ait El Haj et al. 2023; Fu et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Tariq et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Among these computational instruments, the Geodetector exhibits proficiency in effectively recognizing and quantifying the heterogeneity of drivers precipitating small and micro wetland transitions, thereby precisely demystifying the strength and modalities of diverse environmental factors impinging on these transformations. Moreover, the Geodetector exemplifies versatility and pragmatic utility in deciphering the determinants of wetland transitions, rendering it broadly adaptable across an array of wetland systems of disparate types and magnitudes.\\u003c/p\\u003e \\u003cp\\u003eThe Western Jilin Province is located in China\\u0026rsquo;s high latitude northeast, and it is one of important area distributed many wetlands (L et al. 2018). In March of 2022, a significant stride was made when the Chinese government tendered a draft resolution entitled \\\"Guidelines for the Conservation and Management of Small and Micro Wetlands\\\" (1). This seminal document served as a catalyst, fostering a broader understanding of the crucial importance of safeguarding these diminutive wetland ecosystems. Despite the establishment of an array of expansive wetland reserves within this domain (Han et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), research focusing on the nuanced dynamics of small and micro wetlands remains noticeably sparse, with a corresponding dearth of foundational data on these ecosystems. In light of this, our proposed research endeavor intends to harness Landsat satellite imagery spanning the period from 1980 to 2018, complemented by Land Use and Land Cover Change (LULC) classification methodologies, to extract and elucidate wetland data within this geographical locale. Capitalizing on the synergistic use of the Geodetector model, we are committed to embarking on an in-depth exploration of the multitude of factors engendering transformations within the small and micro wetlands in this region, as well as conjecturing about potential trajectories of their future evolution. The primary objectives delineated in this research are three-fold: (1) to collate and dissect the spatio-temporal distribution and dynamic shifts experienced by small and micro wetlands in Western Jilin Province over the past 38 years; (2) to unveil the intrinsic correlations interlinking alterations within these wetlands and broader land use transformations; (3) to engage in a quantitative evaluation of the magnitude of impact rendered by climate change and anthropogenic activities on the transmutations within these small and micro wetlands. By undertaking this investigation, we aspire to accrue a profound understanding of the evolutionary mechanisms governing small and micro wetlands under the duress of climate change and human influences. Concurrently, we anticipate that the methodological approach employed within our study could potentially serve as a guiding beacon for analogous research pursuits concerning small and micro wetlands in diverse regions.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Study Area\\u003c/h2\\u003e \\u003cp\\u003eThe western Jilin Province is situated at the intersection of the Horqin Grassland and Songliao Plain, adjacent to the Great Xing'an Mountains forest area. It includes 11 cities and counties (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) with a total area of approximately 52,300 square kilometers, accounting for 28.1% of the total area of Jilin Province. The area lies within the second major subsidence belt of the Xinhuaxia Formation, characterized by relatively low terrain and gentle topography, with winding and meandering rivers that have insufficient drainage capacity. The climate in this region is classified as a temperate semi-humid continental monsoon climate, with precipitation concentrated in the summer. The precipitation and evaporation rates are relatively close, which is conducive to the formation and maintenance of wetlands. In recent years, the wetlands in western Jilin Province have faced challenges and changes due to agricultural expansion and climate change. With the expansion of agriculture, the surrounding land of wetlands has been reclaimed for farmland and agricultural activities, resulting in wetland degradation and ecosystem disruption. Furthermore, agricultural expansion leads to the reduction and fragmentation of wetland habitats, affecting the migration and reproduction of wetland organisms. Warming temperatures have intensified water evaporation and altered precipitation patterns, which may affect the supply and stability of wetland water resources.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Data Source\\u003c/h2\\u003e \\u003cp\\u003eThe land use data used in this study were obtained from the Resource and Environment Science Data Center of the Chinese Academy of Sciences (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.resdc.cn/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.resdc.cn/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). The data have a spatial resolution of 30 meters and were derived by analyzing Landsat satellite images. Through field validation, the classification accuracy of the data reached 91.2%, meeting the data accuracy requirements of this study. The meteorological data were obtained from the China Meteorological Data Service Center (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://data.cma.cn/\\u003c/span\\u003e\\u003cspan address=\\\"http://data.cma.cn/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). The annual average temperature and annual precipitation data were derived from monthly average temperature and monthly precipitation observations from meteorological stations in the northeastern region where the study area is located. The digital elevation model (DEM) data were sourced from the Geographic Spatial Data Cloud Platform (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.gscloud.cn/).Th\\u003c/span\\u003e\\u003cspan address=\\\"http://www.gscloud.cn/).Th\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003ee GDP data were obtained from the Statistical Yearbook in Jilin Province (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://tjj.jl.gov.cn/\\u003c/span\\u003e\\u003cspan address=\\\"http://tjj.jl.gov.cn/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Methodology\\u003c/h2\\u003e \\u003cp\\u003e(1) Wetland Dynamic Analysis\\u003c/p\\u003e \\u003cp\\u003eBased on the land use classification system from the data source and the actual situation in western Jilin Province, the main land use types considered were paddy fields, dry fields, forests, grasslands, Construction land, water bodies, marshland, saline-alkali land, and unused land. Marshland and water bodies were included in the wetland category for western Jilin Province. The \\\"Extract by Attributes\\\" tool in ArcGIS was used to extract wetland patches. Depending on the vector or raster data type, a new field was added or the \\\"Zonal Statistics\\\" tool was used to calculate the area of each wetland patch. The wetland patches that met the predetermined area range for small-scale wetlands (0.09-8.0 hectares) were selected. Finally, the \\\"Statistics\\\" function in the attribute table was used to calculate the number and corresponding area of the selected small-scale wetlands. This entire process involved the use of various tools in ArcGIS to extract and analyze small and micro wetlands.\\u003c/p\\u003e \\u003cp\\u003e(2) Geodetector Model\\u003c/p\\u003e \\u003cp\\u003eThe Geodetector is a statistical method used to detect spatial heterogeneity and uncover the underlying drivers of the spatial patterns. It aims to evaluate the spatial relationship between independent variables and the dependent variable. The core idea of the Geodetector is based on the assumption that if an independent variable has a significant impact on a dependent variable, their spatial distributions should exhibit similarity. The unique advantage of the Geodetector lies in its ability to identify the interaction effects among factors on the dependent variable. Traditional regression models test the significance of interaction effects by adding interaction terms of two factors. However, the Geodetector assesses the presence, strength, direction, and linearity/non-linearity of interaction effects by calculating and comparing the q-values of individual factors and the q-value of their combination. By employing the Geodetector, researchers can gain insights into the relative contributions of individual factors and their interactions in driving the variations of the dependent variable. This method provides a valuable tool for understanding the complex relationships between spatial variables and can assist in identifying the driving mechanisms behind spatial patterns.\\u003c/p\\u003e \\u003cp\\u003eIn the process of using Geodetectors for correlation analysis, it is necessary to discretize continuous spatial data into grids or zones. Here are the steps involved:\\u003c/p\\u003e \\u003cp\\u003e① Determine the appropriate grid size or zoning method based on the size of the study area and data resolution, balancing accuracy and computational complexity.\\u003c/p\\u003e \\u003cp\\u003e② Grid the spatial data of each influencing factor, ensuring that the grid cell size matches the selected grid size and using the same projection coordinate system and spatial extent.\\u003c/p\\u003e \\u003cp\\u003e③ When aggregating data, assign multiple observation points or samples to their respective grids or zones and calculate statistical indicators such as averages or totals within each zone.\\u003c/p\\u003e \\u003cp\\u003eBy following these steps, suitable discretized spatial data can be prepared for geodetector analysis. This process allows for the exploration of spatial relationships and the identification of driving forces behind the patterns of interest.\\u003c/p\\u003e \\u003cp\\u003eIn this study, the nearest neighbor allocation method is used to resample the indicators. This method is the fastest interpolation method and is suitable for discrete data. It does not change the pixel values. To balance the impact of sample quantity and density on computational efficiency, a sampling grid size of 5 km \\u0026times; 5 km is chosen. Within the study area in western Jilin Province, the influencing factors are uniformly sampled using a 5 km \\u0026times; 5 km grid, resulting in a total of 2081 sampling points. The nearest neighbor allocation method is used to resample the influencing factors at the sampling points, generating raster data.\\u003c/p\\u003e \\u003cp\\u003eThe raster cell values are extracted based on the geometric center of the sampling points and recorded as output features. Then, Geodetectors are used to calculate the impact intensity and interaction effects of the various influencing factors on the changes in small-scale wetlands in western Jilin Province. Two dependent variables are selected in this experiment: the number of small-scale wetlands (Y1) and the area of small and micro wetlands (Y2). The independent variables include paddy fields (X1), dry fields (X2), construction land (X3), saline-alkali land (X4), evaporation (X5), precipitation (X6), aridity index (X7), and GDP (X8). The q-value is calculated to measure the explanatory power of the independent variables (Xi) on the dependent variable (Y).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results and Analysis\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Spatiotemporal changes in small and micro wetlands\\u003c/h2\\u003e \\u003cp\\u003e(1) The temporal evolution of small and micro wetlands\\u003c/p\\u003e \\u003cp\\u003eThrough comparative analysis of the extracted small and micro wetlands from 1980 to 2018 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), it was found that the number and area of small and micro wetlands showed a decreasing trend. From 1980 to 1990, the number and area of small and micro wetlands sharply decreased by 83.1% and 54.0%, respectively. From 1990 to 2010, although the decrease was relatively small, it still showed a downward trend, with a decrease of 29.3% in quantity and 36.5% in area. During the period from 2010 to 2018, although the number of small and micro wetlands rebounded, the area continued to decrease, increasing by 8.7% and decreasing by 4.8%, respectively. Except for Fuyu City, all regions have experienced a significant reduction in the number and area of small and micro wetlands. The degradation of small and micro wetlands in the Da'an City is particularly evident in terms of quantity, with a decrease of 1107 wetlands from 1139 in 1980 to 32 in 2018. In terms of area, the wetland area decreased from 722.20 square kilometers in 1980 to 126.34 square kilometers in 2018, a decrease of 595.86 square kilometers. While wetlands degrade, the average wetland area increases. Compared to larger wetlands, smaller wetlands have a higher risk of loss. The number and area of small and micro wetlands in Fuyu City showed a fluctuating and increasing trend throughout the observation period. From 1980 to 2018, the number of small and micro wetlands in Fuyu City increased by 36, with a growth rate of 42.9%. At the same time, the total area of wetlands has also increased by 28.2 square kilometers, with a growth rate of 29.9%. This may be related to the construction of natural reserves such as the Southwest River Beach Wetland in Fuyu City, which provides a suitable environment and resources for protecting and restoring wetland ecosystems.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e(2) Spatial evolution of small and micro wetlands\\u003c/p\\u003e \\u003cp\\u003eFrom the spatial change graph of small and micro wetlands (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), it can be seen that in the past decades, some areas in western Jilin Province have experienced a decrease in small and micro wetlands. For example, in Da'an City and Qian'an County in the central region, especially in the surrounding cities and farmland areas, many wetlands have been affected by urbanization and agricultural expansion, resulting in reduced or completely disappeared areas. The number and area of wetlands in these areas are showing a downward trend. On the other hand, some regions have seen new additions in the distribution of small and micro wetlands in the Taonan City. For example, the northeastern border area, located in the Songhua River basin, may be affected by water resource management, ecological restoration and protection plans. New small and micro wetlands may be formed through natural succession or artificial restoration measures, providing new wetland habitats for local ecosystems. In the western Jilin Province, there are still some areas where the distribution of small and micro wetlands is relatively stable, that is, the number and area of wetlands have remained basically unchanged in the past few decades. These areas may be affected by better protection measures or natural conditions, allowing wetlands to exist relatively stably ecological functions.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Analysis of driving factors for the evolution of small and micro wetlands\\u003c/h2\\u003e \\u003cp\\u003e(1) Analysis of the explanatory power of a single factor\\u003c/p\\u003e \\u003cp\\u003eBetween 1980 and 1990, the three factors of construction land (0.8693), drought index (0.7853), and saline-alkali land (0.7782) had high explanatory power, indicating that they contributed significantly to the changes in the number of small and micro wetlands during this period. The explanatory power of precipitation (0.6566), GDP (0.5798), evaporation (0.5302), dry fields (0.3457), and paddy fields (0.2382) is relatively low. During this period, with the acceleration of urbanization and the promotion of economic development in the western Jilin Province, a large number of natural wetlands were converted into construction land, resulting in a sharp decrease in the area of small and micro wetlands. The expansion of construction land has led to a transformation in land use, with wetlands being landfilled, reclaimed, and developed to meet the needs of urban expansion and population growth. Between 1990 and 2000, construction land (0.8142), saline-alkali land (0.7067), and precipitation (0.5956) had a high explanatory power. However, the explanatory power of paddy fields (0.4045), GDP (0.2468), evaporation (0.2121), dry fields (0.1453), and drought index (0.1206) is relatively low. Similar to the 1980s, human activities such as the expansion of construction land still have a high contribution to the loss of small and micro wetlands. Between 2000 and 2010, precipitation (0.7353), construction land (0.7227), and saline-alkali land (0.6452) had high explanatory power. The explanatory power of evaporation (0.4790), drought index (0.3039), dry field (0.2927), GDP (0.1655), and paddy field (0.1023) is relatively low. During this period, the decrease in precipitation led to a decrease in water resources and a decrease in water levels in western Jilin Province, which in turn affected the hydrological conditions and water supply of small and micro wetlands. Due to the high dependence of small and micro wetlands on water to maintain their humid ecological environment, the reduction of precipitation may have a certain promoting effect on the degradation of small and micro wetlands. Between 2010 and 2018, precipitation (0.9142), saline-alkali land (0.8500), and GDP (0.5794) had high explanatory power. The explanatory power of construction land (0.5295), evaporation (0.4933), drought index (0.4624), paddy field (0.2214), and dry field (0.1617) is relatively low. Similar to the 2000s and 2010s, natural factors such as increased precipitation still have a high contribution to the loss of small and micro wetlands. During this period, the increase in precipitation provided more water supply for the wetland, improving its hydrological conditions and water level. This increased precipitation helps to increase the water area and volume of wetlands, providing more habitats and survival resources, promoting the prosperity of wetland vegetation and increasing biodiversity.\\u003c/p\\u003e \\u003cp\\u003eOverall, the main factors that affect the changes in the area of small and micro wetlands and their explanatory power vary at different time periods. Throughout all time periods, construction land, saline-alkali land, and precipitation have high explanatory power, indicating that these factors contribute significantly to the changes in the area of small and micro wetlands\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eBetween 1980 and 1990, construction land (0.8272), saline-alkali land (0.7679), and precipitation (0.7190) had high explanatory power, indicating that they contributed significantly to the changes in the area of small and micro wetlands during this period. The explanatory power of evaporation (0.6413), drought index (0.6317), GDP (0.4808), dry fields (0.2792), and paddy fields (0.1645) is relatively low. The 1980s was the peak period of infrastructure construction in the western Jilin Province, including the construction of roads, railways, water conservancy facilities, etc. These infrastructure constructions require a large area of land, including wetland areas. Wetlands used as fill materials for infrastructure construction or directly covered can have adverse effects on the protection of small and micro wetlands. Between 1990 and 2000, precipitation (0.8602), saline-alkali land (0.8291), and construction land (0.7751) had high explanatory power. The explanatory power of GDP (0.4834), evaporation (0.3225), drought index (0.2493), dry fields (0.2457), and paddy fields (0.0793) is relatively low. In the 1990s, the western Jilin Province experienced a decrease in precipitation. Less precipitation leads to a decrease in the supply of surface water and groundwater, resulting in insufficient water supply in small and micro wetlands. Wetland ecosystems have a high dependence on water, and a lack of sufficient water supply will lead to wetland drought and vegetation degradation, thereby promoting wetland degradation. Between 2000 and 2010, saline-alkali land (0.8787), evaporation (0.6921), and GDP (0.6687) had high explanatory power. The explanatory power of dry fields (0.6323), paddy fields (0.4751), drought index (0.3523), construction land (0.3278), and precipitation(0.2935) is relatively low. During this period, the expansion of saline-alkali land and soil degradation have become important reasons for the reduction of small and micro wetlands. The salinization of land leads to high salt concentration in the soil around wetlands, which in turn affects the water quality and vegetation growth of wetlands. The expansion of saline-alkali land not only reduces the area of wetlands, but also leads to the degradation of wetland ecosystems, reduction of plant species, and deterioration of water quality. Between 2010 and 2018, saline-alkali land (0.8843), drought index (0.6865), and precipitation (0.6850) had high explanatory power. The explanatory power of GDP (0.6559), evaporation (0.6320), paddy fields (0.5052), construction land (0.3177), and dry fields (0.2133) is relatively low. During this period, the reduction in the area of small and micro wetlands has decreased, with the main contributing factor still being natural factors such as saline-alkali land.\\u003c/p\\u003e \\u003cp\\u003eOverall, the influencing factors vary across different time periods, with natural environmental factors (such as precipitation, saline-alkali land, evaporation, and drought index) and human activities (such as GDP, dry fields, paddy fields, and construction land) having a high explanatory power on the changes in the quantity of small and micro wetlands. At different time periods, the explanatory power of certain factors may significantly increase or decrease, which may be related to comprehensive factors such as climate change, land use change, and economic development at that time.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e(2) Multifactor interaction analysis\\u003c/p\\u003e \\u003cp\\u003eThe driving factor only exhibits bilinear and nonlinear enhancement. This indicates that the interaction between any two factors, the 8 selected in this study, is greater than any single factor and does not weaken the effect. These results indicate that the comprehensive effect of various factors is stronger in explaining changes in small and micro wetlands than a single factor, and has a more positive driving effect on changes in small and micro wetlands.\\u003c/p\\u003e \\u003cp\\u003eBetween 1980 and 1990, there was a high correlation between factors such as construction land (X3), saline-alkali land (X4), evaporation (X5), precipitation (X6), and drought index (X7), which may collectively affect the reduction of the number of small and micro wetlands. The impact of economic development (X8) on the changes in the number of small and micro wetlands is relatively small. In the analysis of changes in the number of small and micro wetlands between 1990 and 2000, it was mainly found that factors such as construction land (X3), saline-alkali land (X4), and evaporation (X5) have a high correlation with other factors, especially with dry fields (X2) and precipitation (X6). This indicates that these factors may collectively affect the reduction of the number of small and micro wetlands during this period. In addition, the correlation between precipitation (X6) and other factors is relatively high, further indicating the impact of reduced precipitation during this period on the reduction of the number of small and micro wetlands. The correlation between GDP (X8) and other factors is relatively low, which may indicate that during this period, the impact of economic development on the changes in the number of small and micro wetlands is relatively small. In the analysis of the changes in the number of small and micro wetlands between 2010 and 2018, we observed a generally high correlation between precipitation (X6) and other factors, especially with paddy fields (X1), dry fields (X2), and evaporation (X5). This indicates that precipitation has a significant impact on the changes in the number of small and micro wetlands during this period. The correlation between saline-alkali land (X4) and other factors is also high, emphasizing once again the impact of intensified land salinization on the reduction of the number of small and micro wetlands. In addition, the two meteorological factors of evaporation (X5) and drought index (X7) remained highly correlated with other factors during this period, further highlighting the key impact of meteorological conditions on the changes in the number of small and micro wetlands. At the same time, the correlation between GDP (X8) and other factors has increased, especially with dry fields (X2), which may indicate that economic development has a more significant impact on the changes in the number of small and micro wetlands during this period.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eBetween 1980 and 1990, the impact of paddy fields (X1) on the changes in the area of small and micro wetlands was relatively small, and the interaction with other factors was weak. The interaction between dry land (X2) and factors such as construction land (X3), saline-alkali land (X4), and evaporation (X5) is strong, indicating that these factors collectively affect the reduction of small and micro wetland area. In addition, precipitation (X6), drought index (X7), and GDP (X8) also have a high interaction with other factors, indicating that these factors reduce the area of small and micro wetlands. During the period from 1990 to 2000, the analysis of multi factor interaction data from geographic detectors showed that the interaction between paddy fields (X1) and other factors was weak, and the impact on the changes in the area of small and micro wetlands was relatively small. The strong interaction between factors such as dry land (X2), construction land (X3), and saline-alkali land (X4) indicates that these factors collectively affect the changes in the area of small and micro wetlands. At the same time, precipitation (X6) has a strong interaction with other factors during this period, especially with building land (X3) and saline-alkali land (X4). The interaction between evaporation (X5) and other factors is relatively weak, but there is still a certain connection with some factors. In addition, the drought index (X7) and GDP (X8) also had a high interaction with other factors during this period, indicating that they played a role in the changes in the area of small and micro wetlands. During the period from 2000 to 2010, the analysis of multi factor interaction data from geographic detectors showed that the interaction between paddy fields (X1) and other factors was weak, and the impact on the changes in the area of small and micro wetlands was relatively small. The interaction between dry land (X2) and construction land (X3) is very strong, indicating that these factors collectively affect the changes in the area of small and micro wetlands. In addition, the interaction between dry land (X2) and other factors is also significant. The strong interaction between factors such as construction land (X3), saline-alkali land (X4), and precipitation(X6) indicates that they collectively affect the changes in the area of small and micro wetlands. The interaction between evaporation (X5) and other factors is relatively weak, but there is still a certain connection with some factors. Precipitation (X6) has a strong interaction with other factors during this period, especially with building land (X3) and drought index (X7). In addition, during this period, the interaction between drought index (X7) and other factors was also high, indicating that they played an important role in the changes in the area of small and micro wetlands. The interaction between GDP (X8) and other factors is relatively weak, but there is a certain degree of interaction with factors such as precipitation (X6) and drought index (X7). During the period from 2010 to 2018, multi factor interaction analysis data from geographic detectors showed that the interaction between paddy fields (X1) and other factors was still weak, and the impact on changes in the area of small and micro wetlands was relatively small. The interaction between dry land (X2) and construction land (X3) is very strong, and these factors collectively affect the changes in the area of small and micro wetlands. Meanwhile, the interaction between dry land (X2) and other factors is also significant. The interaction between construction land (X3) and factors such as saline-alkali land (X4) and precipitation (X6) remains strong during this period.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eMicroscopic and diminutive wetlands are instrumental in the sustenance of ecosystem functions and the preservation of biodiversity. However, the micro-wetlands in the western Jilin Province have exhibited an unmistakable trajectory of degradation over the preceding four decades. It has been quantified that the spatial extent of wetlands in the northeastern region has contracted by over 20% in the past two decades, coupled with a decline in the environmental quality of these wetlands. The primary factors contributing to wetland degradation include natural catastrophes such as droughts and floods, alongside anthropogenic influences encompassing land development, environmental contamination, and overutilization (Cui et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Pal and Talukdar \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e).Research conducted by Li et al. on the wetland ecosystems in the northeastern region illuminated a dire issue of significant biodiversity reduction within these ecosystems, with habitat loss triggered by wetland degradation implicated in the endangered status of numerous pivotal species(Zhang et al. \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) Analysis by Zhang et al. of the sensitivity of the wetland ecosystems in western Jilin Province emphasized a heightened degree of sensitivity, and proffered strategies for the reversion of cultivated lands to grasslands and wetlands(L et al. \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e2016\\u003c/span\\u003e).In the course of the past several decades, the micro-wetlands of western Jilin Province have undergone profound transformations in landscape patterns and sustained considerable degradation. The extent of these wetlands has dwindled from 2801.68 km\\u003csup\\u003e2\\u003c/sup\\u003e in 1980 to a mere 818.28 km\\u003csup\\u003e2\\u003c/sup\\u003e in 2018, and the number has precipitously plunged from 2604 to 337 during the same period. When scrutinized from the perspective of landscape ecology, these alterations manifest intricate spatio-temporal dynamics. Notably, the distribution of these wetlands has gradually diversified from a concentrated pattern, thereby indicating pronounced spatial heterogeneity. The degradation of micro-wetlands in the Da'an and Qian Gorlos regions has been especially conspicuous. In contrast, the fluctuation in the area and quantity of micro-wetlands in southern regions, such as Fuyu, has been comparatively nominal.As depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e, urban construction land in western Jilin Province has been on a steady expansionary course since 1980. This trend is potentially interrelated with the surge in local industrial development and the progression of urbanization. Despite the overall shrinkage in wetland coverage, the degradation of micro-wetlands has been particularly stark.\\u003c/p\\u003e \\u003cp\\u003eThrough a meticulous analysis of the patterns of land utilization with in western Jilin Province (depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e), it becomes evident that the timespan from 1980 to 2018 has witnessed a perpetual augmentation in construction land and paddy fields, contrasted with a ceaseless diminution in grassland areas. Interestingly, wetlands initially exhibited a proliferation, subsequently superseded by a contraction, intimating a significant metamorphosis in the overall landscape configuration (as portrayed in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e). The insights derived from the analysis are as follows:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eThe paddy fields' landscape has experienced the most pronounced expansion, accruing a cumulative increase of 2885.49 km\\u0026sup2; over the 38 years. The period from 2010 to 2018 is noteworthy due to its substantial expansion-approximately twentyfold relative to the preceding decade. During this time, the spatial extent of paddy fields amplified by 3.53%. The county-level administrative divisions registering the most substantial growth in paddy fields over the 38 years encompass Qian Gorlos (1442.75 km\\u0026sup2;), Zhenlai (610.27 km\\u0026sup2;), and Songyuan (497.61 km\\u0026sup2;), collectively accounting for 88.39% of the total paddy field expansion in western Jilin Province. These regions, characterized by lower terrain, geographical remoteness, and economic underdevelopment, have undergone significant paddy field expansion. It is pertinent to note that Taonan is the sole region in western Jilin Province where paddy fields demonstrated a trend of negative growth (-0.26%). This phenomenon can be attributed to the convergence of certain factors such as geographic isolation, the flatness of terrain facilitating paddy field development, and the local government's aggressive encouragement of agricultural production and substantial agricultural support.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eGrasslands have experienced the most extensive contraction, with a net decrease of 3977.96 km\\u0026sup2; over the 38 years. The regions of Tongyu County and Qian Gorlos County have been subjected to relatively significant contractions, while Fuyu County and Songyuan City have encountered smaller diminutions.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eWetlands have undergone a cycle of initial proliferation followed by a subsequent decline. The increment in the first decade stood at 8.5%, whilst the ensuing decrement rate was 34.08%, culminating in a net decrease of 1076.32 km\\u0026sup2;. The regions with the largest scale of contraction are predominantly localized in Fuyu County and Da'an City.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eWhen considering the potential impact of human activities: there is a conspicuous expansion of artificially exploited land. In terms of natural ecological land, although forested land area has witnessed some proliferation, the overarching trend is one of significant contraction. A palpable correlative synergy exists between the shrinkage of natural ecological land and the expansion of agricultural and construction land.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eWhen scrutinizing the historical trajectory of petite and microscopic wetlands with in western Jilin Province, a distinct dichotomous trend emerges. The initial stage spanning from 1980 to 2000 marked a pronounced contraction in both the quantity and geographic span of these wetlands. Simultaneously, we noted a surge in developed land, emerging as a prominent variable. During this phase, urbanization-induced land fragmentation (Liu et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), ecological degradation (Li et al. 2008), and industrial-agricultural activities (L et al. 2016) were paramount contributors. Notably, areas undergoing rapid urbanization, such as Da'an City and Tongyu County, were subjected to more severe degradation of their wetland ecosystems, an assertion that aligns with prior studies (Birch et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Fei et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Lin et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Mao et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Xiong et al. \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).Transitioning into the subsequent period from 2000 to 2018, climatic elements assumed a significant role in sculpting the transformations of western Jilin Province\\u0026rsquo;s petite and microscopic wetlands. The primary catalysts in this phase were increases in precipitation and temperature. Some reachers (Sun et al. \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) reinforced the idea that shifts in precipitation patterns, propelled by global warming and concurrent temperature increases, imposed substantial disruptions on the wetlands' hydrological balance. These dynamics triggered fluctuations in wetland humidity and accelerated evaporation rates, thereby destabilizing the ecosystem (Fay et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Werner et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003eb).Intriguingly, between 2010 and 2018, a paradoxical trend emerged. Despite a consistent rise in precipitation and temperature in western Jilin Province, there was a surprising increase in the number of petite and microscopic wetlands, while their geographic spread diminished. This counterintuitive phenomenon was clarified by the works of Li et al. (Li and Shi \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) and Wu et al. (Wu et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003eb). They proposed that intensified precipitation predominantly augments the wetland's water supply. In the short term, increased precipitation directly inflates the wetlands' hydrological content through enhanced water influx, thereby proliferating their numbers (Mohamed and Savenije \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Xue et al. \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). However, persistent precipitation could induce a state of hydrological oversaturation within wetlands, provoking a contraction in their spatial extent. This scenario could be predicated on reduced soil solubility in the wetlands, potentially leading to salinization(Gell et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Jolly et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Zheng et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).The results are consistent with those from geographical detectors indicating a high contribution of saline-alkali land.\\u003c/p\\u003e \\u003cp\\u003eUnderstanding the spatiotemporal dynamics of petite and microscopic wetlands necessitates an appreciation of the intricate nexus of multiple influencing factors (Chabudzinski et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Liu et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Rouissi et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Between 1980 to 2000, the dynamics of these wetlands in western Jilin Province were significantly shaped by an upsurge in urbanization and agricultural activities, such as the expansion of farmland and rapid urban development, that epitomized human interventions. As we crossed into the 21st century, the dominance began shifting towards natural elements, particularly climate change and alterations in precipitation patterns, although the influence of human activities persisted. Rather intriguingly, they demonstrated a nonlinear amplifying trend in synergy with the natural factors, together driving changes in these diminutive wetlands. Since 2010, the western Jilin Province has been progressively implementing the Interconneted River System Network projects aimed at enhancing river and lake connectivity, intending to bolster the resilience of extensive wetlands to climate change and anthropogenic disturbances (Suir and Sasser \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). However, the project ignores the special characteristics of small and micro wetlands and fails to meet their water supply needs (Yang et al. \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The confluence of land use practices, climatic conditions, and economic development models further complicates this landscape, creating a complex network of influences shaping these petite and microscopic wetlands. Economic development-driven transitions in land use, such as urbanization and the expansion of agricultural lands, can potentially lead to encroachment upon wetlands (Peng et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), and concurrently, these human-induced land use changes can directly impact the hydrological and ecological functionality of wetlands (Rafiei et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Volik et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Climate change, manifesting as shifts in precipitation patterns and temperature, can impact water resources, instigating further alterations in the wetland environment (Herrera-Pantoja et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; O'Keeffe et al. \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Wilson et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The repercussions extend to influencing agricultural yields, subsequently affecting land use patterns and economic trajectories. Additionally, climate change-induced adaptive economic investments, such as the development of irrigation infrastructure, may reciprocally impact the economic development models (Malek et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Rolim et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The collective and cumulative impact of these interplaying factors can potentially induce a holistic transformation in petite and microscopic wetlands. Consequently, the conservation strategies for these wetlands should adopt an integrative approach, acknowledging and incorporating these intertwined relationships, to develop holistic plans that can both accommodate the challenges of climate change and ensure the preservation of these critical ecosystems.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eBased on remote sensing data and Geodetector Model from 1980 to 2018, the impact of natural factors and human activities on small and micro wetlands in a high latitude region was quantitatively analyzed. The main conclusions are as follows:\\u003c/p\\u003e \\u003cp\\u003e(1) The small and micro wetlands in the high latitude region have suffered serious degradation due to climate change and human activities. This trend, while endemic across the in the high latitude region's wetlands, is notably more pronounced within these specific small and micro wetlands in the Western Jilin Province. The spatial area and number of small and micro wetlands decreased by 70.8% and 87.1% in the Western Jilin Province, respectively.\\u003c/p\\u003e \\u003cp\\u003e(2) Impacts of driving factors on small and micro wetlands are characterized by divergence at different periods. In the period from 1980 to 2000, anthropogenic factors exerted a considerable influence, resulting in significant declines in both the quantity and spatial extent of these wetlands. Conversely, the period from 2000 to 2018 witnessed a more dominant contribution from climatic factors, which seemingly decelerated the degradation rate of the small and micro wetlands and even stimulated a certain degree of recovery in their numbers.\\u003c/p\\u003e \\u003cp\\u003e(3) Due to their inherently small scale and dispersed nature, small and micro wetlands are particularly vulnerable to the impacts of both human activities and climatic changes. The inherent risk is that compared to their larger wetlands, the unique requirements of these smaller wetlands are often overlooked. Therefore, it becomes imperative to devise and implement conservation initiatives specifically tailored towards preserving these small and micro wetlands.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eConflicts Interest:\\u003c/strong\\u003e The authors have no relevant financial or non-financial interests to disclose.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u0026nbsp;\\u003c/strong\\u003eWe are grateful to the staff at Heilongjiang University and the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, for their support and assistance throughout the research process. In addition, we would like to express our gratitude to both the editors and reviewers for their efforts and suggestions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u0026nbsp;\\u003c/strong\\u003eY.B.W., J.X.S, Y.M.W. and P.Q. conceived the idea of the study and wrote the manuscript; Y.B.W., P.Q. and Y.W carried out data collection and analysis; P.Q., Y.W., G.X.Z. and C.L.D contributed valuable analysis and manuscript review; all authors approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003eThis research was supported by Outstanding Young Scientist Project in Jilin Province (20230508099RC), National Natural Science Foundation of China (42371037 and 42371169), Major Science and Technology Projects in Jilin Province（20230303007SF）and National Key Research and Development Program of China (2022YFF1300902).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u0026nbsp;\\u003c/strong\\u003eThe information used in the analysis is accessible from public data sources, including the Resource and Environment Science Data Center (http://www.resdc.cn/), the China Meteorological Data Service Center (http://data.cma.cn/), the Geographic Spatial Data Cloud Platform (http://www.gscloud.cn/), and the Statistical Yearbook in Jilin Province (http://tjj.jl.gov.cn/).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAhn C (2015) Wetlands, 5th ed.Ecological Engineering 82:649-650. http://doi.org/10.1016/j.ecoleng.2015.06.038\\u003c/li\\u003e\\n\\u003cli\\u003eAit El, Haj F, Ouadif, L, Akhssas, A (2023) Simulating and predicting future land-use/land cover trends using CA- Markov and LCM models. 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Marine Pollution Bulletin 185:114373. https://doi.org/10.1016/J.MARPOLBUL.2022.114373\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"wetlands\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"wela\",\"sideBox\":\"Learn more about [Wetlands](https://www.springer.com/journal/13157)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/wela/default.aspx\",\"title\":\"Wetlands\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"small and micro wetlands, changing environment, western Jilin Province, high-latitude region\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4003007/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4003007/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eUnderstanding the long-term dynamics and driving factors of small and micro wetlands is crucial for their management and sustainable development. In this study, we utilized Landsat satellite imagery data from 1980 to 2018 and Geodetector Model to explore the spatiotemporal changes of small and micro wetlands in western Jilin Province, China, considering factors such as land use and climate change. The research findings are as follows. (1) The small and micro wetlands in the western Jilin Province have shown a downward trend in the past 40 years. The area of small and micro wetlands has decreased from 2802km\\u003csup\\u003e2\\u003c/sup\\u003e in 1980 to 818 km\\u003csup\\u003e2\\u003c/sup\\u003e in 2018, and the number has decreased from 2604 in 1980 to 337 in 2018. (2) From a spatial distribution perspective, the micro-wetlands initially exhibited a concentrated pattern but gradually dispersed around, demonstrating significant spatial heterogeneity., respectively. From a spatial distribution perspective, they are mainly distributed in Da'an City, accounting for 42% of the western Jilin province. (3) As time has unfolded, the dynamic evolution of small and micro wetlands has been distinctly influenced by an amalgam of natural environmental factors and human interventions. In particular, human-induced activities, notably agricultural expansion and urbanization processes, emerged as the predominant driving forces during the period from 1980 to 2000. However, while human activities continued to impart their influence, the roles of natural determinants such as precipitation have become progressively more apparent during the period from 2001 to 2018. Importantly, the influences exerted by human activities and natural environmental factors on these wetlands are not standalone; there is a marked interplay between them. This interaction, typically presents a nonlinear amplification among the varied influencing factors. The results of this study provide supportive data and scientific evidence for the ecological restoration and conservation of wetlands.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Spatiotemporal Variation of Small and Micro Wetlands and Their Multiple Responses to Driving Factors in the high-latitude region\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-03-12 09:09:35\",\"doi\":\"10.21203/rs.3.rs-4003007/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2024-03-12T03:09:33+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-03-08T09:32:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"Wetlands\",\"date\":\"2024-03-08T00:33:11+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-03-06T04:27:01+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Wetlands\",\"date\":\"2024-03-05T03:56:51+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"wetlands\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"wela\",\"sideBox\":\"Learn more about [Wetlands](https://www.springer.com/journal/13157)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/wela/default.aspx\",\"title\":\"Wetlands\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"b49dde12-1dbc-408d-b7b1-22ec62a6b849\",\"owner\":[],\"postedDate\":\"March 12th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-12-09T16:10:36+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4003007\",\"link\":\"https://doi.org/10.1007/s13157-024-01882-9\",\"journal\":{\"identity\":\"wetlands\",\"isVorOnly\":false,\"title\":\"Wetlands\"},\"publishedOn\":\"2024-12-04 15:57:41\",\"publishedOnDateReadable\":\"December 4th, 2024\"},\"versionCreatedAt\":\"2024-03-12 09:09:35\",\"video\":\"\",\"vorDoi\":\"10.1007/s13157-024-01882-9\",\"vorDoiUrl\":\"https://doi.org/10.1007/s13157-024-01882-9\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4003007\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4003007\",\"identity\":\"rs-4003007\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}