Spatio-Temporal Dynamics of Ecosystem Service Value Functions in Response to Landscape Fragmentation in Boma-Gambella Trans-Boundary Landscape, Southwest Ethiopia and Eastern South Sudan

Spatio-Temporal Dynamics of Ecosystem Service Value Functions in Response to Landscape Fragmentation in Boma-Gambella Trans-Boundary Landscape, Southwest Ethiopia and Eastern South Sudan | 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 Spatio-Temporal Dynamics of Ecosystem Service Value Functions in Response to Landscape Fragmentation in Boma-Gambella Trans-Boundary Landscape, Southwest Ethiopia and Eastern South Sudan Desalegn Yayeh Ayal, Azemir Berhanu Getahun, Amare Bantider Dagnew This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4259934/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Landscape fragmentation plays a crucial role in determining ecosystem service value functions of landscapes. Understanding the relationship between landscape fragmentation and ecosystem services in areas subjected to environmental vulnerability and biodiversity degradation due to anthropogenic and biophysical drivers is a key for improving ecosystem service functions and their sustainability. The study aims to investigate the Spatio-Temporal dynamics of ecosystem service values functions in response to landscape fragmentation in Boma-Gambella Trans-boundary Landscape, Southwest Ethiopia and East South Sudan. The study applied ArcGIS 10.7, FRGSTAT 4.2 and Benefit Transfer Approach to understand the effect of landscape fragmentation on spatial and temporal changes of ecosystem service value functions. The findings indicate that ecosystem service value functions are negatively associated with the increasing fragmentation of the landscapes into core, perforated, edge, and patch areas. The ArcGIS 10.7 results of the transition matrix confirm that a total of 20321.9 million ha of forest land has been converted to other land use land cover types. The results of FRAGSTAT 4.2 reveal that the core areas of the landscape in particular has been changed from 1.95 million ha in 2009 to 0.88 million ha in 2020. These changes and fragmentation result spatial and temporal changes in ecosystem service value functions of the landscape. The results reveal that there were a total of 627.65 million $ US ha − 1 year − 1 ecosystem service value function change between 2009 & 2020. The results of the study also reveal that regulating, provisioning, supporting and cultural service value functions of the forest land decreased at a net change of 198.05 Million $ US ha − 1 year − 1 , with annual rate of decreasing change (18.0 Million $ US ha − 1 year − 1 ) from 2009 t0 2020. Moreover, regulating service value functions of the forest land significantly decreased with a net change of 127.65 Million $ US ha − 1 year − 1 ) at annual decreasing rate of change (11.6 Million $ US ha − 1 year − 1 ) from 2009 to 2020, indicating a higher susceptibility of the forest land to external factors which have been induced by agricultural land and settlement area expansion. The study, therefore, highlights need for understanding landscape fragmentation impact on ecosystem service value functions and the need to promote conservation and restoration of ecosystem services. The study recommends further investigations using high-resolution satellite imagery; detailed field surveys on the effects of landscape fragmentation on ecosystem service value functions; and facilitate conservation and restoration actions for sustainable utilization of the landscape's biodiversity and ecosystem services at various scales in similar biophysical settings as Boma- Gambella Trans-Boundary Landscape. Landscape fragmentation Ecosystem Functions Benefit Transfer Approach Boma-Gambella Landscape Figures Figure 1 Figure 2 Figure 3 1. Introduction The Global Earth is changing rapidly due to anthropogenic and biophysical factors and these changes are expected to accelerate during the next decades, characterized by loss and fragmentation of natural land cover [14; 21]. Landscapes in Global South, in particular, has been changing [ 22 ], many of these changes have an immediate and strong effect on biodiversity, ecosystem services, on human well-being and amenities of landscapes [ 62 ]. In global system change, land use change and landscape fragmentation have been highlighted as a key human-induced impact on ecosystems [45; 60]. The natural environment is significantly affected by anthropogenic and biophysical factors [54; 31; 64; 63]. These impacts manifest as landscape fragmentation, with negative impacts on ecosystems and biodiversity [65; 59; 32]. The global ecosystems, on which humans and all other species depend, are deteriorating at an unprecedented rate, heavily influenced by landscape fragmentation and modification [5; 40; 23; 35]. The decline in ecosystem services can be attributed to the changing landscape and the resulting fragmentation impact [ 60 ]. This loss and fragmentation has been widely acknowledged for negative effects on many types of biodiversity and ecosystem functions [ 17 ]. Landscape fragmentation, a consequence of the division of large land cover units into patch, edge, perforated, and core areas, can be attributed to both human activity and natural changes [ 47 ]. Consequently, this fragmentation can lead to a reduction in ecological diversity, productivity, functional power, connectedness, and overall consistency [ 6 ]. Thus, conservation of ecosystem services requires an understanding how landscapes are affected by landscape fragmentation [ 32 ]. Therefore, in landscapes, where land use change and fragmentation are largely driven by anthropogenic and biophysical factors, assessing how fragmentation affects the ecosystem services provided by nature is critical [6; 54]. This is particularly important for countries and peoples whose livelihoods are highly dependent on natural resources and biodiversity. Land use change and fragmentation decrease the ability of many ecosystems services, which are the base to support human needs and well-being [ 2 ]. The high pressure of natural resource utilization has led to important changes in ecosystem functions and processes at different scales [11; 21]. Understanding landscapes affected by landscape fragmentation is crucial for preserving ecosystem services [19; 32]. However, landscape fragmentation and its associated effects on ecosystem services have been understudied, with a limited comprehensive study on the global effects 0f landscape fragmentation on of ecosystem services [ 1 ]. Although, ecosystem services are the benefits that people obtain from ecosystems that are indispensable to the well-being of all people in all places and survival of other organisms [ 49 ], realizing the relations between human and ecological well-being and incorporating services into conservation planning and development is just at its beginning [ 37 ]. Although an increasing amount of research has examined how ecosystem services adapt to changes in land use in various regions [7; 51], the spatial arrangement of land that represents ecological processes and functions [ 56 ] receives less emphasis. Moreover, since much has been written about the need to quantify and value ecosystem services [20; 42; 13], there are fewer spatially explicit studies on ecosystem services assessing the tradeoffs between ecosystem services over space and time, especially in East African regions [ 49 ]. Boma-Gambella Trans-boundary Landscape, a protected area between South Sudan and Ethiopia, rich in biodiversity, plants and wildlife, present multiple ecosystems services that can be shared among different users. It is a biodiversity corridor in East African, supporting local communities by providing an adaptable ecosystem and improved livelihoods [ 41 ]. However, the continuous land use land cover transformation and landscape fragmentation due to activities for ecosystem goods and services and lack of responsiveness in landscape governance systems towards considering the voices, concerns and benefits of communities have had impacts on its functions leading to degradation of biodiversity and loss of ecosystem services [ 27 ]. Furthermore, factors such as agricultural and settlement expansion, along with human induced environmental challenges are causing degradation to the ecological characteristics of the landscape. Consequently both governments’ current complications regarding land use policies have resulted in damage to biodiversity and ecosystem services of the landscape. Therefore, land use change and fragmentation processes in Boma-Gambella Trams-boundary Landscape, a landscape recognized as a biodiversity hot spot in Eastern Africa, deserves special attention. Therefore, landscape change, fragmentation and ecosystem service assessments in a multi-scale approach, are highly relevant to understand and implement conservation and restoration strategies [16; 38]. However, multi-scale valuation and monitoring are still far from being integrated regularly in ecosystem service assessments and this gap is larger in Boma-Gambella Trams- boundary Landscape. Although most studies have investigated landscape fragmentation and ecosystem services in various regions, there is a lack of comprehensive research focusing in Boma-Gambella Trans-boundary Landscape. Thus, the research gap lies on the need to evaluate the effects of landscape fragmentation on ecosystem service values. Therefore, there is an urgent need to conduct studies on the spatio-temporal changes in landscape level ecosystem services based on integrating remote sensing data and BTA in order to fill the existing research gaps. Therefore, the study on ecosystem service value functions based remote sensing data and BTA, not only reveal the characteristics of spatial and temporal changes in ecosystem services but also contribute for improving the environment, enhancing human wellbeing, protecting organisms extinction and achieving local, regional, national and global sustainable developments with similar biophysical settings as Boma- Gambella Trans-Boundary Landscape, Southwest Ethiopia and East South Sudan. 2. Materials and Methodological approaches 2.1 Description of the Study Area The trans-boundary Boma-Gambella Eco-region/landscape extends from south-west Ethiopia to southeast Sudan and is comprised of the Gambella National Park of Ethiopia and Boma National Park of Southern Sudan and their immediate surroundings. The area lies between 33 0 0 ' 0'' E to 36 0 0' 0'' E Longitude to 5 0 0' 0'' N-8 0 0' 0'' N Latitude with total area of 2,789,540.76 hectares. Gambella National Park and Boma National Park have officially been designated as protected areas in 2002 and 1977 respectively. As the landscape, climatic conditions and hydrologic features are rather similar on both sides of the national border, the landscape can be referred to as one eco-region. This eco-region’s ecosystems provide significant economic, esthetic and social services, on both sides of the border. From ecological point of view, Boma-Gambella Trans-boundary Landscape is an important biodiversity hot spot, rich in fauna and flora. The Boma-Gambella landscape is a diverse system of wetlands, rivers, savannah, open forest, bush and highland areas and includes Boma National Park in eastern South Sudan and Gambella National Park in South West Ethiopia. The Boma-Gambella landscape is renowned for its rich biodiversity and diverse wildlife populations [ 41 ]. As the migratory species depend on seasonal resources in both countries, their survival increasingly depends on bilateral collaboration on conservation by the authorities and stakeholders in the two countries. The landscape's forest area and hydrology are significant aspects, primarily represented by the vast networks of pristine forest and seasonal and permanent wetland [ 15 ]. The major vegetation types that are observed in the landscape are woodland, wooded grassland and grassland. The Colby Environmental Policy Group [ 10 ] highlighted the importance of water resources such as rivers and wetlands in the biophysical environment of the Boma-Gambella landscape. The Baro River, in particular, serves as a lifeline for human and wildlife populations, providing water for drinking, irrigation, and supporting aquatic ecosystems. Gambella national park consists of many rivers such as the Baro, Akobo and Gilo rivers originate from the highlands of Ethiopia and flow through the lowlands of Gambella national park and the neighboring Boma region, while Boma National Park, consists the Kangen River in the west, River Oboth in the east and River Kurun in the south and the Guom swamps in the north [ 4 ]. Several studies have investigated the climate and weather patterns in the Boma-Gambella landscape. Accordingly, the region experiences a tropical monsoon climate characterized by distinct wet and dry seasons to [ 44 ]. The wet season typically occurs from May to October, with high rainfall and increased river flow. In contrast, the dry season, which lasts from November to April, is characterized by lower precipitation and higher temperatures. These findings are consistent with the research conducted by [ 53 ], who reported a bimodal rainfall pattern in the area. These factors contribute to the unique ecological characteristics of the region unique.se factors contribute to the unique ecological characteristics of the region unique. >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Figure 1 . Boma and Gambella National Parks The biophysical environment of Boma-Gambella Trans-Boundary Landscape is a topic of interest due to its significance in understanding the ecological dynamics and conservation efforts in these areas. Thus, national and international conservation initiatives have shown a great deal of interest in the landscape in an effort to preserve it for the benefit of both people and wildlife. Nonetheless, the lack of a legal status for the region between Boma National Park and Gambella National Park, human encroachment in wildlife habitat, customary hunting of wild animals and encroaching agricultural investments, and the absence of an integrated land use and development plan that addresses the land users' economic, social, and environmental concerns are the main challenges in the trans boundary landscape [ 1 ]. 2.2 Methodology 2.2.1 Methods of LULC classification and validation The LULC change is an aspect of analyzing the alterations of global environment change and effects on the ecosystems [ 8 ]. The LULC maps were generated based on a classification scheme consistent with international standards for global reporting and integration. The United States Geological Survey (USGS) was used to obtain the required satellite images (Table 1 ). Change detection procedures can be characterized by the data transformation procedures and the analysis techniques used to delimit areas of significant changes [ 36 ]. In order to conduct change detection analysis, precise registration of multi-temporal images, precise radiometric and atmospheric calibration must be satisfied [ 33 ]. In this study, therefore, a Landsat Infrared imagery of both multi-spectrally of different Landsat band combination (green, red, near-IR bands, two SWIR bands and a thermal IR band; and multi-temporally, across years were applied to conduct change detection analysis [ 24 ]. The study utilized Landsat 7 ETM + medium resolution (30m) and panchromatic (grey scale 15m) for LULC classification (2009), and Landsat 8 OLI/TIRS medium resolution-blue, green, red and near infrared (30m) resolution and panchromatic (grey scale 15m) for LULC classification (2020) using ERDAS IMAGINE 2015 software. Table 1 Detail information on Landsat images used in the study for the years (2009 & 2020). Satellite Sensor Spatial Resolution (m) Sources Datum Coordinate system Projection Landsat 7 ETM ETM + 30 USGS WGS 1984 WGS 1984 UTM Transverse Mercator Landsat 8 OLI/TIRS 30 USGS WGS 1984 WGS 1984 UTM Transverse Mercator Table 1 Detail information on Landsat images used in the study for the years (2009 & 2020) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> The results of class confusion matrix were obtained on ArcGIS 10.7. The accuracy was verified by randomly selecting 200 sample training points from Google Earth Explorer and comparing them with classification types. The overall user classification accuracy for LULC types for the years 2009 and 2020 were found 82% and 87%, with Kappa coefficient of 0.78 and 0.85 for the years 2009 and 2020 respectively showing a strong degree of accuracy [ 18 ]. . 2.2.2 Methods of analyzing LULC dynamics The change detection technique [18; 55] was used to analyze the dynamics of LULC maps for the years 2009 and 2020, using unsupervised and supervised classification algorisms [ 39 ]. To reduce the spectral reflectance noise, the study applied post classification change detection technique to analyze the dynamics of LULC changes from 2009 to 2020. The land use transition matrix provides detailed class change information [ 52 ]. It describes the change in the number of areas and the trend of transfer between various LULC types in different periods [66] calculated as equation (Eq. 1): \({C}_{i xj }\) = \({A}_{i xj}^{T}\) x \({10}^{n}\) - \({A}_{i xj}^{T+1}\) …………………………………………………………………….………………… (1) Where, \({C}_{i xj }\) refers to the land use type change matrix from the k period to the k + 1 period. \({A}_{i xj}^{K}\) , and \({A}_{i xj}^{K+1}\) refer to the two types of LULC type. In the equation, n is generally 1 or 2. When the number of land use type maps < 10, n is 1; and n is 2 when, 10 < number of land use type maps < 100. \({C}_{i xj }\) is the landscape that is transitioning from category i to category j. The diagonal element ( \({A}_{i xj}^{K}\) ) indicates the area of the landscape with persistence of category i. The horizontal entries ( \({A}_{i xj}^{K+1}\) ) indicate transitions from category i to another category j. Additionally, class-specific changes were calculated, along with total change area and net changes of the entire study area. Specifically, the rate of changes for a given class were determined by dividing the class-specific changes between two time intervals by the number of years between these two observed points in time (Eq. 2 ): $$\begin{array}{cc}\text{A}\text{n}\text{n}\text{u}\text{a}\text{l} \text{r}\text{a}\text{t}\text{e} \text{o}\text{f} \text{L}\text{U}\text{L}\text{C} \text{c}\text{h}\text{a}\text{n}\text{g}\text{e} \left(R\right)=& \frac{{{A}_{k}}^{{T}_{2}}- {{A}_{k}}^{{T}_{1}} }{\text{T}}\end{array}$$ 2 ………………………………………………….………… Where R is the annual rate LULC or fragmentation \({{A}_{k}}^{{T}_{1}}\) is area of LULC type K (ha) in year 1, \({{A}_{k}}^{{T}_{2}}\) is area of LULC type K (ha) in year 2, \(\text{T}\) is the time interval between \({{A}_{k}}^{{T}_{1}}\) and, \({{A}_{k}}^{{T}_{2}}\) in year. 2.2.3 Landscape fragmentation Categories The study applied Fragstat 4.2 software as a model of analysis for the landscape fragmentation categories. The tool separates six different types of landscape fragmentation categories at the spatial scale, including patch, edge, perforated, small core (250 acres), middle core(250–500 acres), and large core(> 500 acres). The FRAGSTATS 4.2 software provides the quantitative results of the defined derived classes [ 34 ]. The study based on [ 3 ] landscape unit definition of landscape fragmentation of Patch, Edge, Perforated, small core ( 500 acres). Table 2 Reference landscape unit definitions Table 2 Reference landscape unit definitions Landscape fragmentation categories Definition Patch A discontinuous area at spatial or temporal domains or environmental condition which is relatively homogeneous / Forest pixels that comprise a small forested area surrounded by non-forested land cover/. Edge An edge represents an area where the rapid changes of observed value are found or where the change rate is very high. /Forest pixels that define the boundary between core forest and large non forested land cover features/. Perforated The edge habitat generated by a small area of non-forest habitat which is enclosed by core area /Forest pixels that define the boundary between core forest and relatively small clearings (perforations) within the forested landscape/. Small core (< 250 acres) Internal area of any landscape /Forest patches that are smaller than 250acres/. Medium core (250–500 acres) Internal area of any landscape /Forest patches between 250 and 500 acres/. Large core (> 500 acres) The internal area of patches after the elimination of edge buffer /Forest patches greater than 500 acres/. >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 2.2.4 Methods for ESV f Valuation The analysis for Ecosystems Service Value functions (ESV f ) of LULC types and fragmented categories were based on benefit transfer approach [ 12 ]. The coefficients of tropical areas and Central Asia on regional estimates of ESV f using data provided by [ 29 ] were used. The ESV f used in this study, therefore, were calculated using coefficients obtained through transfer approach from other research works [9; 25; 48] to investigate the effects of landscape fragmentation on ecosystem service functions of ESV f of LULC types. The biomes used as proxies for the fragmented categories of LULC classes were based on Kreuter et al., [ 26 ]. Table 3 LULC categories and ecosystem service coefficients in US $ ha − 1 y − 1 based on the modified estimates Table 3 LULC categories and ecosystem service coefficients in US $ ha − 1 y − 1 based on the modified estimates Types Category Tropical Forests Cropland River/Lake Desert Swamps Villages Provisioning service Food production 32 187.56 41 0.0 106 0.0 Raw materials 51.24 0.0 0.0 0.0 0.0 0.0 Genetic resources 41 0.0 0.0 0.0 0.0 0.0 Water supply 8 0.0 2117 0.0 0.0 0.0 Regulating services Gas regulation 13.68 0.0 0.0 0.0 0.0 0.0 Climatic regulation 223 0.0 0.0 0.0 0.0 0.0 Disturbance regulation 5 0.0 0.0 0.0 0.0 0.0 Waste treatment 136 0.0 431.5 0.0 918 0.0 Erosion control 245 0.0 0.0 0.0 0.0 0.0 Pollination 7.27 14 0.0 0.0 0.0 0.0 Water regulation 6 0.0 5445 0.0 9322 0.0 Biological control 0.0 24 0.0 0.0 0.0 0.0 Supporting services Nutrient cycling 184.4 0.0 0.0 0.0 0.0 0.0 Soil formation 10 0.0 0.0 0.0 0.0 0.0 Habitat/refugia 17.3 0.0 0.0 0.0 0.0 0.0 Cultural services Culture 2 0.0 0.0 0.0 0.0 0.0 Recreation 4.8 0.0 69 0.0 0.0 0.0 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> . The equivalent value coefficients of each ES were used to calculate ecosystem service value functions. The values of individual ecosystem service value functions provided by LULC types and fragmented categories were calculated [26; 29] as: \({ESV}_{fk}\) = \(\sum {(A}_{k}\) * \({VC}_{k}\) )……………………………………………………………..………... (3) Where, \({ESV}_{fk}\) is estimated total ecosystem service value functions of LULC type K , \({A}_{k}\) is the area (ha) of LULC type K , and VC k the value coefficient of function (USD ha − 1 yr − 1 ) for each LULC type K. To compute effects of landscape fragmentation on ESV f of LULC types, \(ESVf\) of LULC types and \(ESVf\) of fragmented categories such as patch, edge, perforated, small core area, medium core area and large core area were estimated based on the coefficient of each biome as: $$\begin{array}{c}\text{E}\text{S}\text{V}f=({\text{E}\text{S}\text{V}\text{f}}_{\text{p}}\text{*} {\text{V}\text{C}}_{P}+{\text{E}\text{S}\text{V}\text{f}}_{e}*{{\text{V}\text{C}}_{e}+ \text{E}\text{S}\text{V}\text{f}}_{Pr}*{VC}_{Pr}{+\text{E}\text{S}\text{V}\text{f}}_{Sc}{* VC}_{SC}+{ESVf}_{mc}* {VC}_{mc }+{ESVf}_{lc}*{VC}_{lc})\end{array}$$ 4 ……. Where, \(\text{E}\text{S}\text{V}\text{f}\) is the total ecosystem service values of fragmented categories and \({ \text{E}\text{S}\text{V}\text{f}}_{\text{p}}\) , \({\text{E}\text{S}\text{V}\text{f}}_{e},\) \({ESVf}_{Pr}\) , \({ ESVf}_{SC }\) , and \({ ESVf}_{LC}\) represent ecosystem service functions of patch, edge, perforated, small core area, medium core area and large core area respectively. The difference between estimated total ecosystem service value functions of LULC type K (Eq. 3) and ecosystem service values of fragmented categories (Eq. 4 ) were computed as: \({\text{E}\text{S}\text{V}f}_{ek }={ESVf}_{k}\) - \({ESVf}_{C}\) ………………………………………………...……………………...……………. (5) Where, \({\text{E}\text{S}\text{V}f}_{ek }\) = effect of fragmented categories on ecosystem service value functions of LULC type \(k\) . \({ESVf}_{k}\) = estimated total ecosystem service value function of LULC type K \({ESVf}_{C}\) = estimated total ecosystem service value function of fragmented categories 2.2.5 Analysis of Sensitivity for Ecosystem Services The method for determining the value of ecosystem services was developed by multiplying the area of a given land use category by the relevant value coefficient. However, due to the unpredictable, dynamic, and nonlinear nature of social-ecological systems, the results obtained using Benefit Transfer Approach (BTA) has low resolution, large deviation, and significant uncertainty [30; 50; 57]. Therefore, coefficients the proxies' biomes were used to quantify ecosystem service functions [ 29 ]. Although the biomes employed as proxies, land use might be used as a substitute for ecosystem services. Therefore, the value coefficients were adjusted by ± 50% using sensitivity analysis (26). By adjusting the ESV coefficient per unit area of each land use type by ± 50.0%, the CSs of ESV of landscapes can be calculated to maintain the reliability of ecosystem service valuation as: $$\begin{array}{cc}CS =& \frac{{(ESV}_{j }-{ESV}_{i} /{ESV}_{i}}{{VC}_{jk}-{VC}_{ik }/{VC}_{ik}}\end{array}$$ 6 ………………………………………………………………………………………… Where CS = coefficient of sensitivity, ESV is estimated ESV, VC = value coefficient, i and j are initial and adjusted value. If value is CS ≤ 1, then ESV considered inelastic and when CS ≥ 1 is considered elastic relative to value coefficient (Q. Zhao et al., 2020). 3. Results AND Discussions 3.1 Distribution and dynamics of LULC change Figure 2 shows the spatial representation of LULC types of Boma- G ambella Trans-boundary Landscap e . The area of each LULC types, percentage share and annual rate of change were extracted from Landsat images (2009 & 2020). The result shows six LULC types: forest land, agricultural land, water bodies, wetland, bare land and settlement. It has been observed that the landscape was dominated by forest land covering 52.3% of the total area of the landscape in 2009 followed by agricultural land (36%), bare land (7.7%) and wetland (3.7%). The forest land that was 52.3% in 2009 has been reduced to 45.1% in 2020 with an annual rate of decreasing change 40.35% (18247.1 ha/yr). Table 4 LULC Types, Area and percent Share and annual rate of change for Boma-Gambella trans-boundary landscape (2009–2020) Table 4 LULC Types, Area and percent Share and annual rate of change for Boma-Gambella trans-boundary landscape (2009–2020) LULC Types 2009 2020 Annual rate of LULC change (ha/yr) Annual rate of LULC change (%) Area (Ha.) Area (%) Area (Ha.) Area (%) Forest Land 1,460,133.67 52.3 1,259,416.10 45.1 -18247.1 40.35 Agricultural land 1,004,378.79 36 1,233,877.86 44.2 20863.6 40.93 Water bodies 8910.26 0.3 6,123.20 0.2 -253.4 0.17 Bare land 208,246.63 7.5 186,370.20 6.7 -1988.8 6.02 Wet land 104,491.18 3.7 97,172.26 3.5 -665.4 3.16 Settlement 3,380.23 0.1 6,581.14 0.2 291 0.19 Total 2,789,540.76 100 2,789,540.76 100 Source: ArcGIS 10.7 (2022) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Moreover, water bodies, bare land and wetlands have been experienced a decreasing annual rate of change, while agricultural land that was 36% in 2009 has been increased to 44.2% in 2020 with an annual rate of increasing change (20863.6 ha/yr), followed by settlement areas with an annual rate of increasing change (291.0 ha/yr) between 2009 & 2020 (Table 4 ). The highest annual rate of decreasing change for forest land has been due to expansion agricultural land and settlement areas. >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Source: ArcGIS 10.7 (2022) Figure 2 LULC Change maps for 2009 and 2020 3.2 Land-Use and Land-Cover Change Transition Matrix Table 5 shows the cross-tabulation LULC change matrix from 2009 to 2020. The result revealed that forest land, water bodies, and wetland experienced an annual decreasing rate of conversion, while, agricultural land, bare land and sentiment areas showed an annual increasing rate of class change. Forest land showed the largest annual decreasing rate conversion (124,178.5ha/yr), while, agricultural land revealed the highest increasing rate of conversion (43,424.6ha/year). Table 5 Land use/Land cover change transition matrix Land use land cover types in 2009 Land use land cover types in 2020 LULC Classes Forest land Agricultural land Water bodies Bare land Wetland Settlement Area (ha) Area (ha) Area (ha) Area (ha) Area (ha) Area (ha) Forest land - 63,293.20 273.06 12,499.90 3377.16 2054.52 Agricultural land 70,401.90 - 729.18 201,874 22,424.60 214.74 Water bodies 582.03 937.2 - 317.09 567 958 Bare land 16,209.30 46,435.10 8971 - 5484 30,157 Wetland 5,688.60 18,263.40 109.44 2321.73 - 7,007.10 Settlement 1288.65 1,144.70 198.2 432.4 345.8 - Total 94,170.48 130,073.60 10,007.82 204,945.22 28,821.40 38,336.84 Annual Rate of Conversion -20321.9 17902.9 9451.2 188002.5 19987.6 37738.6 Source: ArcGIS (2022) Table 5 Land use/Land cover change transition matrix >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 3.3 Analysis of Landscape Fragmentation Landscape fragmentation categories were extracted using ARC-GIS 10.7 and landscape fragmentation categories were calculated using FRAGSTATS 4.2. Accordingly, the largest fragmented land use type was forest land, accounting 2177368.02ha and 1506759.2ha in 2009 and 2020 respectively, while agricultural land was the second largest fragmented landscape, accounting 101925.3ha in 2009 and 245145.49 ha in 2020 (Table 6 ). Table 6 Area in hectare for each fragmented class (2009 &2020) Year Fragmented Classes Forest land Agricultural land Bare land Water bodies Wetland Settlement Total Area (ha) Area (ha) Area (ha) Area (ha) Area (ha) Area (ha) Area (ha) 2009 Patch 35544.69 17773.53 0.44 6.14 17.19 0.23 53342.2 Edge 102194.99 51297.76 24.16 3.37 62.7 0.66 153584 Perforated 65291.54 32853.97 32.2 4.44 85.39 0 206926 Core( 500acres) 1950020 0 0 0 0 0 1950020 Total 2177368 101925.3 56.8 13.95 165.28 0.89 2279530 2020 Patch 88072.7 44539.11 968.61 20.72 70.4 9.11 133681 Edge 188548.17 49738.08 1219.78 35.44 108.22 7.25 239657 Perforated 301438.96 150868.3 609.78 46.18 85.31 2.05 373338 Core( 500acres) 877525.58 0 0 0 0 0 877526 Total 1506759.2 245145.5 2798.17 102.34 263.93 18.41 1755088 Source: ArcGIS 10.7 and FRAGSTAT 4.2 (2022) Table 6 Area in hectare for each fragmented class (2009 &2020) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Figure 3 . Fragmentation categories of Boma-Gambella landscape (2009–2020) Table 7 also shows the fragmented categories, percent share and annual rate of change for Boma-Gambella Trans-boundary Landscape (2009–2020). The result revealed that the core area (> 500 acres) has been the dominant class (81.7% & 52.4%) in 2009 and 2020 respectively. Table 7 Landscape Fragmentation change (2009–2020) Fragmented Classes Area (ha) 2009 Area (%) Area (ha) 2020 Area (%) Change Area (ha) 2009–2020 Change Area (%) Annual rate of change (ha/yr) Patch 53342.2 2.2 133681 8 80338.8 5.3 7303.5 Edge 153584 6.4 239657 14.3 86073 6.9 7824.8 Perforated 206926 8.7 373338 22.3 166412 12.2 15128.4 Core( 500acres) 1950020 81.7 877526 52.4 -1072494 -35.5 -97499.5 Total 2388189 1675376 -712813.18 -9.3 -64801.3 Source: ArcGIS 10. 7 and FRAGSTAT 4.2 (2022) Table 7 Landscape Fragmentation change (2009–2020) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> The perforated area has been the second dominant fragmented class (8.7% & 22.3%) in 2009 and 2020 respectively. In 2009, large core area occupied 1950020 ha of land, while in 2020 it was significantly reduced to 877526ha. Large core area lost 1072494 ha within these eleven-years with annual rate of decreasing change (97499.5 ha/yr). In 2009, the perforated area was 206926ha and increased dramatically in 2020 to 373338ha with annual rate of increasing change (15128.4ha/yr), thereby degrading the perforated areas into more edges area. The edge area in 2009 was 153584ha and increased slightly in 2020 to 239657ha with annual rate of increasing change (7824.8ha/yr). Perforated area shows an increase of 12.2% from 8.7% in 2009 to 22.3% in 2020. The increase in the perforated area shows that more of the core areas of the landscape has been degraded continuously and converted in perforated areas. Patch area 53342.2ha in 2009 has been transformed in to 133681 ha in 2020, with an increase of 5.3% from 2.3% in 2009 7.6% in 2020 and the overall change shows that landscape fragmentation has been at the core areas of the forest land and large areas of the forest land were more fragmented compared with other LULC types. The transformation of 2.3% of patch area in 2009 to 8% in 2020; 6.4% edge area in 2009 to 14.4% in 2020 and 8.7% of perforated area in 2009 to 22.3% in 2020 indicate the fragmentation of core areas of the landscape. The highest rate of fragmentation has been observed in large core area (> 500acres) of the forest land between 2009 and 2020 at a decreasing change rate of 35.5%. This highest rate fragmentation of core area has been due to the increasing change in perforated areas, edge areas, and patch areas in the study landscape. 3.4 Ecosystem Services Values of functions (ESV f ) of Fragmented Categories Table 8 demonstrates the ecosystem service value functions (ESV f ) of fragmented categories LULC types in the Boma-Gambella landscape. The results reveal that the landscape has total ESV f of 2173.2 million $ US ha − 1 year − 1 and 1545.6 million $ US ha − 1 year − 1 in 2009 and 2020 respectively. Table 8 Landscape fragmentation Categories and estimated ESV f ( Million $ US ha − 1 year − 1 ) Year Fragmented Classes Forest land Agricultural land Bare land Water bodies Wetland Settlement Total 2009 Patch 35.10 4.01 0.0 0.05 0.18 0.0 39.30 Edge 100.80 11.57 0.0 0.03 0.65 0.0 113.10 Perforated 64.40 7.41 0.0 0.04 0.88 0.0 72.80 Core( 500acres) 1924.10 0.0 0.0 0.0 0.0 0.0 1924.10 Total 2148.40 22.99 0.0 0.11 1.71 0.0 2173.20 2020 Patch 86.90 10.05 0.0 0.17 0.73 0.0 97.80 Edge 186.00 11.22 0.0 0.29 1.12 0.0 198.70 Perforated 297.40 34.03 0.0 0.37 0.88 0.0 332.70 Core( 500acres) 865.80 0.0 0.0 0.0 0.0 0.0 865.80 Total 1486.70 55.3 0.0 0.83 2.73 0.0 1545.60 Change (2009–2020) Patch 51.80 6.04 0.0 0.12 0.55 0.0 58.51 Edge 85.20 -0.35 0.0 0.26 0.47 0.0 85.58 Perforated 233.00 26.62 0.0 0.33 0.0 0.0 259.95 Core( 500acres) -1058.30 0.0 0.0 0.0 0.0 0.0 -1058.30 Total -661.70 32.31 0.0 0.72 1.02 0.0 -627.65 Source: Computed based on ArcGIS 10. 7, FRAGSTAT 4.2 and BTA (2022) Table 8 Landscape fragmentation Categories and estimated ESV f ( Million $ US ha − 1 year − 1 ) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Among LULC types,, forest cover fragmented categories provide the greatest ESV f (2148.4 million $ US ha − 1 year − 1 ) and (1486.7 million $ US ha − 1 year − 1 ) in 2009 and 2020 respectively, followed by fragmented categories of agricultural land, 23 million $ US ha − 1 year − 1 in 2009 and 55.3 million $ US ha − 1 year − 1 in 2020, while wetland accounts the third largest provider of ecosystem service value functions (1.7 million $ US ha − 1 year − 1 and 2.7 million $ US ha − 1 year − 1 ) in 2009 and 2020 respectively. The results also show that the core areas the forest land has highest ESV f , 1948.1 Million $ US ha − 1 year − 1 in 2009 and 916.3 Million $ US ha − 1 year − 1 in 2020. The study also depicts the ESV f change between 2009 and 2020 in Boma-Gambella Trans-boundary landscape. The results reveal that there were a total decrease of 627.65 $ US ha − 1 year − 1 in ESV f from 2009 to 2020, with the highest decreasing change accounted for by core areas (> 500 acres) of forest land (1058.30 million $ US ha − 1 year − 1 ). The results therefore, confirmed that landscape fragmentation in Boma-Gambella Trans-boundary landscape has significantly changed the ESV f of the landscape from 2009 to 2020. 3.5 Spatial Variations and Temporal Changes of Ecosystem Service Value Functions (ESV f) Table 9 demonstrates the ecosystem service value functions change at temporal and spatial scale in Boma-Gambella Trans- Boundary Landscape. The study results show that there were spatial variations in ESV f in the last 11 years. Accordingly, forest land and agricultural areas have the greatest regulating, provisioning, supporting and cultural service value functions. The water bodies and wetlands provide minimal service value functions as compared with forest land and agricultural land areas. Table 9 Spatial Variations and Temporal Changes in ESV f (Million $ US ha − 1 year − 1 ) Year ESV Functions Forest land Agriculture/crop land Bare land Water bodies Wetland Settlement Total 2009 Provisioning service 193.09 132.82 0.0 1.18 0.40 0.0 327.49 Regulating services 928.57 638.73 0.0 5.67 0.80 0.0 1573.77 Supporting services 309.11 212.63 0.0 1.89 0.30 0.0 523.92 Cultural services 9.93 6.83 0.0 0.06 0.50 0.0 17.32 Total 1440.7 991.01 0.0 8.79 2.00 0.0 2442.5 2020 Provisioning service 166.55 163.17 0.0 0.81 0.60 0.0 331.12 Regulating services 800.93 784.68 0.0 3.89 1.20 0.0 1590.7 Supporting services 266.62 261.21 0.0 1.3 0.60 0.0 529.73 Cultural services 8.56 8.39 0.0 0.04 0.80 0.0 17.8 Total 1242.65 1217.45 0.0 6.04 3.20 0.0 2469.35 Net Change (2009–2020) Provisioning service -26.54 295.99 0.0 -0.37 0.20 0.0 269.28 Regulating services -127.65 145.95 0.0 -1.77 0.40 0.0 16.93 Supporting services -42.49 -48.58 0.0 -0.59 0.90 0.0 -90.77 Cultural services -1.36 1.56 0.0 -0.02 0.30 0.0 0.48 Total -198.05 394.91 0.0 -2.75 1.80 0.0 195.92 Annual rate of Change (2009–2020) Provisioning service -2.4 26.9 0.0 -0.03 0.02 0.0 24.5 Regulating services -11.6 13.3 0.0 -0.16 0.04 0.0 1.54 Supporting services -3.9 -4.4 0.0 -0.05 0.08 0.0 -8.25 Cultural services -0.1 0.1 0.0 0.00 0.03 0.0 0.04 Total -18.0 35.9 0.0 -0.25 0.16 0.0 17.81 Source: Computed based on ArcGIS 10. 7, FRAGSTAT 4.2 and BTA (2022) Table 9 Spatial Variations and Temporal Changes in ESV f (Million $ US ha − 1 year − 1 ) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Moreover, ecosystem service value functions of the landscape change at temporal scales. The results of the study reveal that regulating, provisioning, supporting and cultural service value functions of the forest land decreased at a net change of 198.05 Million $ US ha − 1 year − 1 , with annual rate of decreasing change (18.0 Million $ US ha − 1 year − 1 ) from 2009 t0 2020. The results of the study also reveal that regulating service value functions of the forest land significantly decreased with a net change of 127.65 Million $ US ha − 1 year − 1 ) at annual decreasing rate of change (11.6 Million $ US ha − 1 year − 1 ) from 2009 to 2020, indicating a higher susceptibility of the forest land to external interference. Moreover, the results show that regulating, provisioning and supporting service value functions of water bodies decreased with a net change of 2.75 Million $ US ha − 1 year − 1 at annual decreasing change rate of 0.25 Million $ US ha − 1 year − 1 from 2009 to 2020. Accordingly, gas regulation, climate regulation, and hydrological regulation services reduced due to the decrease in forest land and water bodies. The study results also confirm that supporting services value functions of forest land and agricultural land decreased by 42.49 Million $ US ha − 1 year − 1 and 48.58 Million $ US ha − 1 year − 1 at annual decreasing rate of 3.9 and 4.4 Million $ US ha − 1 year − 1 from 2009 t0 2020 respectively. These results suggest that nutrient cycling, soil formation and habitat/refugia service value functions of forest land and agricultural land were significantly reduced due to agricultural investment projects, settlement expansion and population encroachments in the study landscape. Although ESV f from natural land cover has decreased in the present study area due to landscape fragmentation, there has been an increase in ESV f from agricultural land investment. The results show that ESV f of agricultural land increased from 991.01 Million $ US ha − 1 year − 1 in 2009 to 1217.45 Million $ US ha − 1 year − 1 in 2020. As confirmed by most FGDs and kIIs, however, much of the services from agricultural land investment have been left for export earnings for governments, foreign and local investors, with little or no benefit for local communities. 3.6 Coefficient of Sensitivity The coefficient of sensitivity (CS) analysis in this study for LULC change, landscape fragmentation and ecosystem service function change study (2009–2020) were all less than ≤ 1. The sensitivity of forest land, and wetlands were larger with sensitivity coefficient < 1. Table 10 Coefficient of sensitivity for ecosystem service functions (ESV f ) in Boma-Gambella landscape (2009 & 2020) Table 10 Coefficient of sensitivity for ecosystem service functions (ESV f ) in Boma-Gambella landscape (2009 & 2020) Change in Valuation Coefficient (VC) The Effect of Changing VC by ± 50% from the Original Value 2009 2020 % CS % CS Forest Cover 11.1 0.64 11 0.59 Agricultural land 10.2 0.5 10.1 0.54 Water body 0 0 0 0 Bare land 9.5 0.5 9.4 0.5 Wet land 9.5 0.5 9.4 0.5 Settlement 0 0 0 0 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> The sensitivity coefficient of forest cover was highest, 1- 0.8 for ESV f assessment, this indicated that when the forest ESV f coefficient increased or decreased by 1%, the total ESV f increased or decreased by 1%- 0.8% for 2009–2020 (Table 10 ). The sensitivity analysis's findings, therefore, indicated that the ecosystem service function (ESV f) estimation for the study area were rather inelastic with respect to the value coefficients, which implies that the estimation of ESV f were reliable and trustworthy, which is in line with other studies [25; 25;55; 61]. 4. Conclusion The study based on spatial and temporal changes of ecosystem service value functions in response to the effects of landscape fragmentation in Boma-Gambella Trans boundary Landscape, Southwest Ethiopia. The findings of the study, assert a significant transformation of LULC types and fragmentation of landscape categories. Accordingly, ecosystem service value functions change at temporal scales, from 2009 to 2020 due to fragmentation of the landscapes in to core, perforated, edge, and patch areas and vary across LULC types. The study findings are, therefore, in confirmation with [43; 60] that landscape fragmentation is widely known as the principal causes of biodiversity depletion and loss of ESV f . The analysis reveals a decline in ecological land from 2009 to 2020 and significant changes in landscape patterns. Anthropogenic factors, such as agricultural investment and settlement expansion have remained driver of land use land cover transformation and landscape fragmentation and result in spatial variations and temporal changes in ecosystem service functions. The study highlights the vital importance of understanding how fragmentation of natural land cover affects ecosystem service functions. Incorporating these effects into ecosystem service assessments is critical to the development of effective tools that can help landscapes to provide multiple ecosystem services. Therefore, the need for empirical research into the exact nature of the relationships between fragmentation and ecosystem service functions is critical. Moreover, as the ecosystem services concept is increasingly incorporated into decision-making and planning activities, the need to improve understanding of ecosystem service function at the landscape scale is fundamentally important. Thus, for addressing landscape fragmentation effect on ecosystem service functions, a high-resolution satellite images, detailed field survey and a more integrated analysis is essential for sustainable utilization of the landscape resources. The information generated in this study will, therefore, be crucial in developing viable policies, strategies and programs to promote sustainable use of landscapes in similar biophysical and socio-economic settings as Boma-Gambella Trans-boundary Landscape, Southwest Ethiopia and East South Sudan. Declarations Declaration of Competing Interest: The authors declare that we have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper. CreDiT Authorships Taxonomy: Azemir Berhanu Getahun : Writing- original draft and formal analysis; Amare Bantider Dagnew: Conceptualization, Methodology, Visualization and Investigation. Desalegn Yayeh Ayal: Supervision, Writing-Reviewing, Editing and Validation. Data availability : Data will be made available on request. Acknowledgment s : The authors are grateful to HoAREC&N and Addis Ababa University (AAU) for financial support for data collection for the first author of this paper. References Benjamin Grey, Cummings Colin & Evangelides Ellen. Exploring the Effects of Land Use and Land Cover Change on White-Eared Kob Migration in the Gambella Region, Ethiopia. Environmental Policy. 2013.Missing Players in Environmental Governance. Bennett, E. M., Cramer, W., Begossi, A., Cundill, G., Díaz, S., Egoh, B. N. Woodward, G. Linking biodiversity, ecosystem services, and human well-being: three challenges for designing research for sustainability. Current Opinion in Environmental Sustainability. 2015; 14, 76–85. doi:10.1016/j.cosust.2015.03.007 Bindajam, A. A., Mallick, J., Mahato, S., Talukdar, S., & Alquadhi, S. Assessing landscape fragmentation effects on ecosystem services in a semi-arid mountainous environment: A case study on abha watershed, saudi arabia. Applied Ecology and Environmental Research . 2021; 19 (3), 2519–2539. https://doi.org/10.15666/aeer/1903_25192539 BirdLife International. Important Bird Area factsheet: Gambella National Park, Ethiopia. 2009. http://www.birdlife.org. Boulanger, Yan & Pascual, Jesus. Boreal forests will be more severely affected by projected anthropogenic climate forcing than mixed wood and northern hardwood forests in eastern Canada. Landscape Ecology. 2021; 36. 10.1007/s10980-021-01241-7. Chen, Wanxu & Chi, Guangqing & Li, Jiangfeng.. Ecosystem Services and Their Driving Forces in the Middle Reaches of the Yangtze River Urban Agglomerations, China. International Journal of Environmental Research and Public Health. 2020; 17. 3717. 10.3390/ijerph17103717. Christine F, Susanne F, Anke W, Lars K, Franz M. Assessment of the effects of forestland use strategies on the provision of ecosystem services at regional scale. Journal of Environmental Management. 2013; 127: 96–116. Clerici, N., Paracchini, M. L., & Maes, J. Land-cover change dynamics and insights into ecosystem services in European stream riparian zones. Ecohydrology & Hydrobiology. 2014; 14(2), 107–120. doi:10.1016/j.ecohyd.2014.01.002 Coates, D., Pert, P. L., Barron, J., Muthuri, C., Nguyen-Khoa, S., Boelee, E., & Jarvis, D. I. Water-related ecosystem services and food security. Managing Water and Agroecosystems for Food Security . 2013; 29–41. https://doi.org/10.1079/9781780640884.00 Colby Environmental Policy Group. Food, Forests, and Livelihoods in East Africa. Waterville, Maine: Colby College Environmental Studies Program. 2015. Costanza, R., d'Arge, R., de Groot, R. The value of the world's ecosystem services and natural capital. 1997; Nature 387. https://doi.org/10.1038/387253a0 Costanza, R., de Groot, R., Sutton, P., van der Ploeg, S., Anderson, S. J., Kubiszewski, I., Farber, S., & Turner, R. K. Changes in the global value of ecosystem services. Global Environmental Change . 2014; 26 (1), 152–158. https://doi.org/10.1016/j.gloenvcha.2014.04.002 Cowling, R.M., Egoh, B., Knight, A.T., O’Farrel, P., Reyers, j., Rouget’ll, B., Roux, M., Weiz, D.J., Wilhelm-Rechman, A.A. An operational model for mainstreaming ecosystem services for implementation. Proceedings of the National Academy of Science. 2008; 105, 9483–9488. DeFries R S, Foley J A and Asner G P. Land-use choices: balancing human needs and ecosystem function Frontiers. Ecol. Environ. 2004; 2 249–57. Di Baldassarre, G., Elshamy, M., van Griensven, A., Soliman, E., Kigobe, M., Ndomba, P. Uhlenbrook, S. Future hydrology and climate in the River Nile basin: a review. Hydrological Sciences Journal. 2011; 56(2), 199–211. doi:10.1080/02626667.2011.557378. Echeverria C, Coomes D, Salas J, Rey-Benayas JM, Lara A, Newton A. Rapid deforestation and fragmentation of Chilean temperate forests. Biol Conserv. 2006; 130:481–494 Ewers RM, Didham RK. Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews. 2006; 81(1):117-142. doi:10.1017/S1464793105006949 Fahad, K. H., Hussein, S., & Dibs, H. Spatial-Temporal Analysis of Land Use and Land Cover Change Detection Using Remote Sensing and GIS Techniques. IOP Conference Series: Materials Science and Engineering. 2020; 671 (1). https://doi.org/10.1088/1757-899X/671/1/012046 Fahrig, Lenore. Ecological Responses to Habitat Fragmentation Per Se. Annual Review of Ecology, Evolution, and Systematics. 2017; 48. 10.1146/annurev-ecolsys-110316-022612 Fisher, B., Turner, R.K. Ecosystem services: classification for valuation. Biological Conservation. 2008; 141, 1167–1169. Foley, J., Ramankutty, N., Brauman, K. et al. Solutions for a cultivated planet. Nature. 2011; 478, 337–342. https://doi.org/10.1038/nature10452 Geist, H.J., Lambin, E.F. Proximate causes and underlying driving forces of tropical deforestation. Bioscience. 2002; 52, 143–150. Gerland, Patrick & Raftery, Adrian & Ševčíková, Hana & Li, Nan & Gu, Danan & Spoorenberg, Thomas & Alkema, Leontine & Fosdick, Bailey & Chunn, Jennifer & Lalic, Nevena & Bay, Guiomar & Buettner, Thomas & Heilig, Gerhard & Wilmoth, John. World Population Stabilization Unlikely This Century. Science. 2014; 10.1126/science.1257469. Green, K. Change Metters, Photogrammetric Engineering and Remote Sensing. 2011; 77:4, 305:309. Kindu, M., Schneider, T., Teketay, D., & Knoke, T. Science of the Total Environment Changes of ecosystem service values in response to land use / land cover dynamics in Munessa – Shashemene landscape of the Ethiopian highlands. Science of the Total Environment. 2016; 547 , 137–147. https://doi.org/10.1016/j.scitotenv.2015.12.127 Kreuter, U. P., Harris, H. G., Matlock, M. D., & Lacey, R. E. Change in ecosystem service values in the san antonio area, Texas. Ecological Economics . 2001; 39 (3), 333–346. https://doi.org/10.1016/S0921-8009(01)00250-6 Kussia, A., Tolossa, D., & Abate, E. A Crisis of the Green Gold: A Case Study of Large Scale Agricultural Investment in Gambella Region, Ethiopia. Eastern Africa Social Science Research Review. 2020; 36(2), 53–94. doi:10.1353/eas.2020.0006 Lambin, E.F. & Turner, Brittney & Geist, H.J. & Agbola, Samuel & Angelsen, Arild & Bruce, J.W. & Coomes, Oliver & Dirzo, R. & Fischer, Günther & Folke, Carl & George, P.S. & Homewood, K. & Imbernon, J. & Leemans, Rik & Li, Xiubin & Moran, Emilio & Mortimore, M. & Ramakrishnan, P.S. & Richards, J.F. & Xu, Jianchu. The causes of land-use and land-cover change: Moving beyond the myths. Global Environmental Change-Human and Policy Dimensions. 2001; 11 (2001). - ISSN 0959-3780. 11. Li, J., Chen, H., Zhang, C., & Pan, T. Variations in ecosystem service value in response to land use/land cover changes in Central Asia from 1995-2035. PeerJ , 2019; 2019 (9), 1–22. https://doi.org/10.7717/peerj.7665 Limburg, K. E., O’neill, R. V., Costanza, R., & Farber, S. Complex systems and valuation. Ecological Economics . 2002; 41 (3), 409–420. https://doi.org/10.1016/S0921-8009(02)00090-3 Lin Yang, Feixue Shen, Lei Zhang, Yanyan Cai, Fangxin Yi, Chenghu Zhou.. Quantifying influences of natural and anthropogenic factors on vegetation changes using structural equation modeling: A case study in Jiangsu Province, China, Journal of Cleaner Production. 2021; Volume 280, Part 2, 124330, ISSN 0959 6526. https://doi.org/10.1016/j.jclepro.2020.124330. Liu, Jiajia & Wilson, Maxwell & Hu, Guang & Liu, Jinliang & Wu, Jianguo & Yu, Mingjian. How does habitat fragmentation affect the biodiversity and ecosystem functioning relationship? Landscape Ecology. 2018; 33. 10.1007/s10980-018-0620-5. Lu, D., Mausel, P., Brondizio, E. and Moran, E. Change detection techniques, International Journal of Remote Sensing. 2004; 25:12, 2365-2401. MacLean, M.G., & Congalton, R. G., “Mapping and analysis of fragmentation in South eastern New Hampshire”, A special joint symposium of ISPRS technical commission iv & Auto Carto in conjunction with ASPRS/CaGIS. 2010; Fall Specialty Conference, Orlando, Florida. Mahmoud, Shereif & Gan, Thian Yew. Impact of anthropogenic climate change and human activities on environment and ecosystem services in arid regions. Science of the Total Environment. 2018; 633. 1329-1344. 10.1016/j.scitotenv.2018.03.290. Mas, J.F. Monitoring land-cover changes: a comparison of change detection techniques, International Journal of Remote Sensing. 1999; 20:1, 139-152. McDonald, R.I. Ecosystem Service demand and supply along the urban-to-rural gradient. Journal of Conservation Planning. 2009; 5, 1–14. Miranda A, Altamirano A, Cayuela L, Pincheira F, Lara A. Different times, same story: native forest loss and landscape homogenization in three physiographical areas of south-central of Chile. Appl Geogr. 2015; 60:20–28. doi: 10.1016/j.apgeog.2015.02.016. Mishra, K., Sam, A. G., Miranda, M. J., & Diiro, G. M. Gender and Dynamics of Technology Adoption: Evidence from Uganda. Agricultural & Applied Economics Association and Western Agricultural Economics Association. 2015; Annual Meeting . Mitchell, Matthew & Suárez Castro, Andrés & Martinez-Harms, Maria & Maron, Martine & Mcalpine, Clive & Gaston, Kevin & Johansen, Kasper & Rhodes, Jonathan.. Reframing landscape fragmentation's effects on ecosystem services. Trends in Ecology & Evolution. 2015; 30. 10.1016/j.tree.2015.01.011. Mohammed, S. L., & Behailu, T. Impacts of human activities on wildlife: The case of Nile Lechwe (Kobus megaceros) Gambella National Park, Southwest Ethiopia. International Journal of Biodiversity and Conservation. 2019; 11(1), 48–57. doi:10.5897/ijbc2017.1144 Naidoo, R., Balmford, A., Costanza, R., Fisher, B., Green, R.E., Lehner, B., Malcom, T.R., Ricketts, T.H. Global mapping of ecosystem services and conservation priorities. Proceedings of the National Academy of Science. 2008; 105, 9495–9500. Nyssen, J.; Poesen, J.; Moeyersons, J. Deckers, J. Haile, M. and Lang A. Human impact on the environment in the Ethiopian and Eritrean highlands-a state of the art, Earth-Science Reviews. 2004; vol. 64, no. 3-4. Owen‐Smith, N., Hopcraft, G., Morrison, T., Chamaillé‐Jammes, S., Hetem, R., Bennitt, E., & Van Langevelde, F. Movement ecology of large herbivores in African savannas: current knowledge and gaps. Mammal review . 2020; 50 (3), 252-266. Pal, S., Singha, P., Lepcha, K., Debanshi, S., Talukdar, S., & Saha, T. K.. Proposing multicriteria decision based valuation of ecosystem services for fragmented landscape in mountainous environment. Remote Sensing Applications: Society and Environment. 2020; 100454. doi:10.1016/j.rsase.2020.100454 Phoomirat, R., Disyatat, N.R., Park, T.Y. . Rapid assessment checklist for green roof ecosystem services in Bangkok, Thailand. Ecol Process 9. 2020; https://doi.org/10.1186/s13717-020-00222-z. Shapiro, A.C., Aguilar-Amuchastegui, N., Hostert, P. Using fragmentation to assess degradation of forest edges in Democratic Republic of Congo. Carbon Balance Manage. 2016; 11. https://doi.org/10.1186/s13021-016-0054-9 Shiferaw, H., Alamirew, T., Kassawmar, T., & Zeleke, G. Evaluating ecosystems services values due to land use transformation in the Gojeb watershed, Southwest Ethiopia. Environmental Systems Research . 2021; 10 (1). https://doi.org/10.1186/s40068-021-00227-3. Silvestri, S., Zaibet, L., Said, M. Y., & Kifugo, S. C. Valuing ecosystem services for conservation and development purposes: A case study from Kenya. Environmental Science & Policy. 2013; 31, 23–33. doi:10.1016/j.envsci.2013.03.008 Solomon, N., Segnon, A. C., & Birhane, E. Ecosystem service values changes in response to land-use/land-cover dynamics in dry afromontane forest in northern Ethiopia. International Journal of Environmental Research and Public Health . 2019; 16 (23), 7–11. https://doi.org/10.3390/ijerph16234653 Su CH, Fu BJ. Evolutions of ecosystem services in the Chinese Loess Plateau under climatic and land use changes. Global and Planetary Change. 2013. 101:119–128. Terfa, B. K., Chen, N., Liu, D., Zhang, X., & Niyogi, D. Urban expansion in Ethiopia from 1987 to 2017: Characteristics, spatial patterns, and driving forces. Sustainability (Switzerland) . 2019; 11(10), 1–21. https://doi.org/10.3390/su11102973 Tesfaye, K., Gbegbelegbe, S., Cairns, J., Shiferaw, B., Prasanna, B., Sonder, K., Boote, K., Makumbi, D., Robertson, R. Maize systems under climate change in sub-Saharan Africa. International Journal of Climate Change Strategies and Management. 2015; 7. 247-271. 10.1108/IJCCSM-01-2014-0005. Tian, C., Cheng, Ll. & Yin, T. Impacts of anthropogenic and biophysical factors on ecological land using logistic regression and random forest: A case study in Mentougou District, Beijing, China. J. Mt. Sci. 2022; 19, 433–445. 31https://doi.org/10.1007/s11629-021-7022-x Tolessa, T., Senbeta, F., & Kidane, M. The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia. Ecosystem Services . 2017; 47–54. https://doi.org/10.1016/j.ecoser.2016.11.010 Turner MG, Gardner RH, O’Neill RV. Landscape Ecology in theory and practice. 2001. New York: Springer-Verlag. Turner, R. K., Paavola, J., Cooper, P., Farber, S., Jessamy, V., & Georgiou, S. Valuing nature: Lessons learned and future research directions. Ecological Economics . 2003; 46 (3), 493–510. https://doi.org/10.1016/S0921-8009(03)00189-7 Valenteet, J.J., Gannon, D.G., Hightower, J. Toward conciliation in the habitat fragmentation and biodiversity debate. Landsc Ecol. 2023; 38, 2717–2730. https://doi.org/10.1007/s10980-023-01708-9 Wang, H., Zhang, M., Wang, C., Wang, K., Wang, C., Li, Y., Bai, X., & Zhou, Y. Spatial and Temporal Changes of Landscape Patterns and Their Effects on Ecosystem Services in the Huaihe River Basin, China. Land . 2022; 11(4), 1–19. https://doi.org/10.3390/land11040513 Wang, Yahui & Dai, Erfu & Le, Yin & Ma, Liang. Land use/land cover change and the effects on ecosystem services in the Hengduan Mountain region, China. Ecosystem Services. 2018; 34. 55-67. 10.1016/j.ecoser.2018.09.008. Watson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo, D.J.,Dokken, D.J. (Eds.). Land Use, Land use Change and Forestry. A Special Report of the Intergovernmental Panel on Climate Change (IPCC). 2000. Cambridge University Press, Cambridge. Xu Y, Bi R, Li Y. Effects of anthropogenic and natural environmental factors on the spatial distribution of trace elements in agricultural soils. Ecotoxicol Environ. 2022; Saf 249:114436. 2022; doi: 10.1016/j.ecoenv. 114436. Epub. PMID: 36525951. Yang, L., Shen, F., Zhang, L., Cai, Y., Yi, F., & Zhou, C. Quantifying influences of natural and anthropogenic factors on vegetation changes using structural equation modeling: a case study in Jiangsu Province, China. Journal of Cleaner Production. 2020; 124330. doi:10.1016/j.jclepro.2020.124330 Zambrano, L., Aronson, M. F. J., & Fernandez, T. The Consequences of Landscape Fragmentation on Socio-Ecological Patterns in a Rapidly Developing Urban Area: A Case Study of the National Autonomous University of Mexico. Frontiers in Environmental Science.2019,; 7. doi:10.3389/fenvs.2019.00152. Zhao, Q., Wen, Z., Chen, S., Ding, S., & Zhang, M. Quantifying Land Use/Land Cover and Landscape Pattern Changes and Impacts on Ecosystem Services. International Journal of Environmental Research and Public Health. 2019; 17(1), 126. doi:10.3390/ijerph17010126. 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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-4259934","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":295110410,"identity":"b78597ae-1148-4beb-9592-836a193a1c22","order_by":0,"name":"Desalegn Yayeh Ayal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDCCA0D8gMGCgR/ESSggVksCgwSDZAOIYUCKFgMQg4EYLXzH2x9+SGyTkDc+vzrxwwMDBnl+sQP4tUieOWMsAdRiuO3G280SQIcZzpydgF+LwY0cBpAWxm03zm4AaUkwuE1Iy/3nj38AtdhvnnF28w/itNxgMAPZkriBv3cbcbZInskxs0g4J5E84wbvNosEAwnCfuE7fvzxjQ9lNrb9/Wc33/xRYSPPL01ACwJIgFVKEKscBPgPkKJ6FIyCUTAKRhIAAOFMSJgQM4L6AAAAAElFTkSuQmCC","orcid":"","institution":"Addis Ababa University","correspondingAuthor":true,"prefix":"","firstName":"Desalegn","middleName":"Yayeh","lastName":"Ayal","suffix":""},{"id":295110413,"identity":"a6855332-5859-4c04-88b7-d2c8bdf1957b","order_by":1,"name":"Azemir Berhanu Getahun","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Azemir","middleName":"Berhanu","lastName":"Getahun","suffix":""},{"id":295110415,"identity":"3989bc4a-74f9-4390-90ac-09d0cbbed361","order_by":2,"name":"Amare Bantider Dagnew","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Amare","middleName":"Bantider","lastName":"Dagnew","suffix":""}],"badges":[],"createdAt":"2024-04-13 00:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4259934/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4259934/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55307519,"identity":"7f5dc739-bb78-4df4-a54f-ab079a135cff","added_by":"auto","created_at":"2024-04-25 13:51:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":318455,"visible":true,"origin":"","legend":"\u003cp\u003eLocation Map of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4259934/v1/1d810afd257d46a5deedd8b4.png"},{"id":55308080,"identity":"e76030c3-254c-40be-9731-73e16140fb8b","added_by":"auto","created_at":"2024-04-25 13:59:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":849224,"visible":true,"origin":"","legend":"\u003cp\u003eLULC Change maps for 2009 and 2020\u003c/p\u003e\n\u003cp\u003eSource: ArcGIS 10.7 (2022)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4259934/v1/ff71c699e24ceda6f05d65c7.png"},{"id":55307520,"identity":"75c4123f-79dd-4037-aa02-1df4650b4e52","added_by":"auto","created_at":"2024-04-25 13:51:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186073,"visible":true,"origin":"","legend":"\u003cp\u003eFragmentation categories of Boma-Gambella landscape (2009-2020)\u003c/p\u003e\n\u003cp\u003eSource: ArcGIS 10.7 and FRAGSTAT 4.2 (2022)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4259934/v1/8b9de942287bee1a40d903a6.png"},{"id":55308622,"identity":"63259d08-10ec-4744-9e06-521ac47e67d0","added_by":"auto","created_at":"2024-04-25 14:07:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1794383,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4259934/v1/f3b2941d-953f-4dd9-aaac-30cdc0cb0c8a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatio-Temporal Dynamics of Ecosystem Service Value Functions in Response to Landscape Fragmentation in Boma-Gambella Trans-Boundary Landscape, Southwest Ethiopia and Eastern South Sudan","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Global Earth is changing rapidly due to anthropogenic and biophysical factors and these changes are expected to accelerate during the next decades, characterized by loss and fragmentation of natural land cover [14; 21]. Landscapes in Global South, in particular, has been changing [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], many of these changes have an immediate and strong effect on biodiversity, ecosystem services, on human well-being and amenities of landscapes [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In global system change, land use change and landscape fragmentation have been highlighted as a key human-induced impact on ecosystems [45; 60]. The natural environment is significantly affected by anthropogenic and biophysical factors [54; 31; 64; 63]. These impacts manifest as landscape fragmentation, with negative impacts on ecosystems and biodiversity [65; 59; 32].\u003c/p\u003e \u003cp\u003eThe global ecosystems, on which humans and all other species depend, are deteriorating at an unprecedented rate, heavily influenced by landscape fragmentation and modification [5; 40; 23; 35]. The decline in ecosystem services can be attributed to the changing landscape and the resulting fragmentation impact [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This loss and fragmentation has been widely acknowledged for negative effects on many types of biodiversity and ecosystem functions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLandscape fragmentation, a consequence of the division of large land cover units into patch, edge, perforated, and core areas, can be attributed to both human activity and natural changes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Consequently, this fragmentation can lead to a reduction in ecological diversity, productivity, functional power, connectedness, and overall consistency [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thus, conservation of ecosystem services requires an understanding how landscapes are affected by landscape fragmentation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, in landscapes, where land use change and fragmentation are largely driven by anthropogenic and biophysical factors, assessing how fragmentation affects the ecosystem services provided by nature is critical [6; 54]. This is particularly important for countries and peoples whose livelihoods are highly dependent on natural resources and biodiversity.\u003c/p\u003e \u003cp\u003eLand use change and fragmentation decrease the ability of many ecosystems services, which are the base to support human needs and well-being [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The high pressure of natural resource utilization has led to important changes in ecosystem functions and processes at different scales [11; 21]. Understanding landscapes affected by landscape fragmentation is crucial for preserving ecosystem services [19; 32]. However, landscape fragmentation and its associated effects on ecosystem services have been understudied, with a limited comprehensive study on the global effects 0f landscape fragmentation on of ecosystem services [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough, ecosystem services are the benefits that people obtain from ecosystems that are indispensable to the well-being of all people in all places and survival of other organisms [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], realizing the relations between human and ecological well-being and incorporating services into conservation planning and development is just at its beginning [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although an increasing amount of research has examined how ecosystem services adapt to changes in land use in various regions [7; 51], the spatial arrangement of land that represents ecological processes and functions [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] receives less emphasis. Moreover, since much has been written about the need to quantify and value ecosystem services [20; 42; 13], there are fewer spatially explicit studies on ecosystem services assessing the tradeoffs between ecosystem services over space and time, especially in East African regions [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBoma-Gambella Trans-boundary Landscape, a protected area between South Sudan and Ethiopia, rich in biodiversity, plants and wildlife, present multiple ecosystems services that can be shared among different users. It is a biodiversity corridor in East African, supporting local communities by providing an adaptable ecosystem and improved livelihoods [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, the continuous land use land cover transformation and landscape fragmentation due to activities for ecosystem goods and services and lack of responsiveness in landscape governance systems towards considering the voices, concerns and benefits of communities have had impacts on its functions leading to degradation of biodiversity and loss of ecosystem services [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, factors such as agricultural and settlement expansion, along with human induced environmental challenges are causing degradation to the ecological characteristics of the landscape. Consequently both governments\u0026rsquo; current complications regarding land use policies have resulted in damage to biodiversity and ecosystem services of the landscape. Therefore, land use change and fragmentation processes in Boma-Gambella Trams-boundary Landscape, a landscape recognized as a biodiversity hot spot in Eastern Africa, deserves special attention. Therefore, landscape change, fragmentation and ecosystem service assessments in a multi-scale approach, are highly relevant to understand and implement conservation and restoration strategies [16; 38]. However, multi-scale valuation and monitoring are still far from being integrated regularly in ecosystem service assessments and this gap is larger in Boma-Gambella Trams- boundary Landscape.\u003c/p\u003e \u003cp\u003eAlthough most studies have investigated landscape fragmentation and ecosystem services in various regions, there is a lack of comprehensive research focusing in Boma-Gambella Trans-boundary Landscape. Thus, the research gap lies on the need to evaluate the effects of landscape fragmentation on ecosystem service values. Therefore, there is an urgent need to conduct studies on the spatio-temporal changes in landscape level ecosystem services based on integrating remote sensing data and BTA in order to fill the existing research gaps. Therefore, the study on ecosystem service value functions based remote sensing data and BTA, not only reveal the characteristics of spatial and temporal changes in ecosystem services but also contribute for improving the environment, enhancing human wellbeing, protecting organisms extinction and achieving local, regional, national and global sustainable developments with similar biophysical settings as Boma- Gambella Trans-Boundary Landscape, Southwest Ethiopia and East South Sudan.\u003c/p\u003e"},{"header":"2. Materials and Methodological approaches","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Description of the Study Area\u003c/h2\u003e \u003cp\u003eThe trans-boundary Boma-Gambella Eco-region/landscape extends from south-west Ethiopia to southeast Sudan and is comprised of the Gambella National Park of Ethiopia and Boma National Park of Southern Sudan and their immediate surroundings. The area lies between 33\u003csup\u003e0\u003c/sup\u003e0\u003csup\u003e'\u003c/sup\u003e0'' E to 36\u003csup\u003e0\u003c/sup\u003e 0' 0'' E Longitude to 5\u003csup\u003e0\u003c/sup\u003e0' 0'' N-8\u003csup\u003e0\u003c/sup\u003e0' 0'' N Latitude with total area of 2,789,540.76 hectares. Gambella National Park and Boma National Park have officially been designated as protected areas in 2002 and 1977 respectively. As the landscape, climatic conditions and hydrologic features are rather similar on both sides of the national border, the landscape can be referred to as one eco-region. This eco-region\u0026rsquo;s ecosystems provide significant economic, esthetic and social services, on both sides of the border. From ecological point of view, Boma-Gambella Trans-boundary Landscape is an important biodiversity hot spot, rich in fauna and flora. The Boma-Gambella landscape is a diverse system of wetlands, rivers, savannah, open forest, bush and highland areas and includes Boma National Park in eastern South Sudan and Gambella National Park in South West Ethiopia. The Boma-Gambella landscape is renowned for its rich biodiversity and diverse wildlife populations [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. As the migratory species depend on seasonal resources in both countries, their survival increasingly depends on bilateral collaboration on conservation by the authorities and stakeholders in the two countries. The landscape's forest area and hydrology are significant aspects, primarily represented by the vast networks of pristine forest and seasonal and permanent wetland [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The major vegetation types that are observed in the landscape are woodland, wooded grassland and grassland.\u003c/p\u003e \u003cp\u003eThe Colby Environmental Policy Group [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] highlighted the importance of water resources such as rivers and wetlands in the biophysical environment of the Boma-Gambella landscape. The Baro River, in particular, serves as a lifeline for human and wildlife populations, providing water for drinking, irrigation, and supporting aquatic ecosystems. Gambella national park consists of many rivers such as the Baro, Akobo and Gilo rivers originate from the highlands of Ethiopia and flow through the lowlands of Gambella national park and the neighboring Boma region, while Boma National Park, consists the Kangen River in the west, River Oboth in the east and River Kurun in the south and the Guom swamps in the north [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Several studies have investigated the climate and weather patterns in the Boma-Gambella landscape. Accordingly, the region experiences a tropical monsoon climate characterized by distinct wet and dry seasons to [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The wet season typically occurs from May to October, with high rainfall and increased river flow. In contrast, the dry season, which lasts from November to April, is characterized by lower precipitation and higher temperatures. These findings are consistent with the research conducted by [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], who reported a bimodal rainfall pattern in the area. These factors contribute to the unique ecological characteristics of the region unique.se factors contribute to the unique ecological characteristics of the region unique.\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003eFigure\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Boma and Gambella National Parks\u003c/p\u003e \u003cp\u003eThe biophysical environment of Boma-Gambella Trans-Boundary Landscape is a topic of interest due to its significance in understanding the ecological dynamics and conservation efforts in these areas. Thus, national and international conservation initiatives have shown a great deal of interest in the landscape in an effort to preserve it for the benefit of both people and wildlife. Nonetheless, the lack of a legal status for the region between Boma National Park and Gambella National Park, human encroachment in wildlife habitat, customary hunting of wild animals and encroaching agricultural investments, and the absence of an integrated land use and development plan that addresses the land users' economic, social, and environmental concerns are the main challenges in the trans boundary landscape [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methodology\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Methods of LULC classification and validation\u003c/h2\u003e \u003cp\u003eThe LULC change is an aspect of analyzing the alterations of global environment change and effects on the ecosystems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The LULC maps were generated based on a classification scheme consistent with international standards for global reporting and integration. The United States Geological Survey (USGS) was used to obtain the required satellite images (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Change detection procedures can be characterized by the data transformation procedures and the analysis techniques used to delimit areas of significant changes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In order to conduct change detection analysis, precise registration of multi-temporal images, precise radiometric and atmospheric calibration must be satisfied [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, therefore, a Landsat Infrared imagery of both multi-spectrally of different Landsat band combination (green, red, near-IR bands, two SWIR bands and a thermal IR band; and multi-temporally, across years were applied to conduct change detection analysis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The study utilized Landsat 7 ETM\u003csup\u003e+\u003c/sup\u003e medium resolution (30m) and panchromatic (grey scale 15m) for LULC classification (2009), and Landsat 8 OLI/TIRS medium resolution-blue, green, red and near infrared (30m) resolution and panchromatic (grey scale 15m) for LULC classification (2020) using ERDAS IMAGINE 2015 software.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetail information on Landsat images used in the study for the years (2009 \u0026amp; 2020).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatellite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpatial Resolution (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDatum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoordinate system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProjection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 7 ETM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETM +\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWGS 1984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWGS 1984 UTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTransverse Mercator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLI/TIRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWGS 1984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWGS 1984 UTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTransverse Mercator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Detail information on Landsat images used in the study for the years (2009 \u0026amp; 2020)\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003eThe results of class confusion matrix were obtained on ArcGIS 10.7. The accuracy was verified by randomly selecting 200 sample training points from Google Earth Explorer and comparing them with classification types. The overall user classification accuracy for LULC types for the years 2009 and 2020 were found 82% and 87%, with Kappa coefficient of 0.78 and 0.85 for the years 2009 and 2020 respectively showing a strong degree of accuracy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Methods of analyzing LULC dynamics\u003c/h2\u003e \u003cp\u003eThe change detection technique [18; 55] was used to analyze the dynamics of LULC maps for the years 2009 and 2020, using unsupervised and supervised classification algorisms [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To reduce the spectral reflectance noise, the study applied post classification change detection technique to analyze the dynamics of LULC changes from 2009 to 2020.\u003c/p\u003e \u003cp\u003eThe land use transition matrix provides detailed class change information [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. It describes the change in the number of areas and the trend of transfer between various LULC types in different periods [66] calculated as equation (Eq.\u0026nbsp;1):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({C}_{i xj }\\)\u003c/span\u003e \u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{i xj}^{T}\\)\u003c/span\u003e\u003c/span\u003e x \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({10}^{n}\\)\u003c/span\u003e\u003c/span\u003e -\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{i xj}^{T+1}\\)\u003c/span\u003e\u003c/span\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (1)\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{i xj }\\)\u003c/span\u003e\u003c/span\u003e refers to the land use type change matrix from the \u003cem\u003ek\u003c/em\u003e period to the \u003cem\u003ek\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1 period. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{i xj}^{K}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{i xj}^{K+1}\\)\u003c/span\u003e\u003c/span\u003e refer to the two types of LULC type. In the equation, n is generally 1 or 2. When the number of land use type maps\u0026thinsp;\u0026lt;\u0026thinsp;10, n is 1; and n is 2 when, 10\u0026thinsp;\u0026lt;\u0026thinsp;number of land use type maps\u0026thinsp;\u0026lt;\u0026thinsp;100. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{i xj }\\)\u003c/span\u003e\u003c/span\u003eis the landscape that is transitioning from category i to category j. The diagonal element (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{i xj}^{K}\\)\u003c/span\u003e\u003c/span\u003e) indicates the area of the landscape with persistence of category i. The horizontal entries (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{i xj}^{K+1}\\)\u003c/span\u003e\u003c/span\u003e) indicate transitions from category i to another category j. Additionally, class-specific changes were calculated, along with total change area and net changes of the entire study area. Specifically, the rate of changes for a given class were determined by dividing the class-specific changes between two time intervals by the number of years between these two observed points in time (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\begin{array}{cc}\\text{A}\\text{n}\\text{n}\\text{u}\\text{a}\\text{l} \\text{r}\\text{a}\\text{t}\\text{e} \\text{o}\\text{f} \\text{L}\\text{U}\\text{L}\\text{C} \\text{c}\\text{h}\\text{a}\\text{n}\\text{g}\\text{e} \\left(R\\right)=\u0026amp; \\frac{{{A}_{k}}^{{T}_{2}}- {{A}_{k}}^{{T}_{1}} }{\\text{T}}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eR\u003c/em\u003e is the annual rate LULC or fragmentation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{A}_{k}}^{{T}_{1}}\\)\u003c/span\u003e\u003c/span\u003e is area of LULC type \u003cem\u003eK\u003c/em\u003e (ha) in year 1, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{A}_{k}}^{{T}_{2}}\\)\u003c/span\u003e\u003c/span\u003e is area of LULC type \u003cem\u003eK\u003c/em\u003e (ha) in year 2, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{T}\\)\u003c/span\u003e\u003c/span\u003e is the time interval between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{A}_{k}}^{{T}_{1}}\\)\u003c/span\u003e\u003c/span\u003eand, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{A}_{k}}^{{T}_{2}}\\)\u003c/span\u003e\u003c/span\u003ein year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Landscape fragmentation Categories\u003c/h2\u003e \u003cp\u003eThe study applied Fragstat 4.2 software as a model of analysis for the landscape fragmentation categories. The tool separates six different types of landscape fragmentation categories at the spatial scale, including patch, edge, perforated, small core (250 acres), middle core(250\u0026ndash;500 acres), and large core(\u0026gt;\u0026thinsp;500 acres). The FRAGSTATS 4.2 software provides the quantitative results of the defined derived classes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The study based on [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] landscape unit definition of landscape fragmentation of Patch, Edge, Perforated, small core (\u0026lt;\u0026thinsp;250 acres), medium core (250\u0026ndash;500), large core (\u0026gt;\u0026thinsp;500 acres).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Reference landscape unit definitions\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReference landscape unit definitions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape fragmentation categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA discontinuous area at spatial or temporal domains or environmental condition which is relatively homogeneous / Forest pixels that comprise a small forested area surrounded by non-forested land cover/.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn edge represents an area where the rapid changes of observed value are found or where the change rate is very high. /Forest pixels that define the boundary between core forest and large non forested land cover features/.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerforated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe edge habitat generated by a small area of non-forest habitat which is enclosed by core area /Forest pixels that define the boundary between core forest and relatively small clearings (perforations) within the forested landscape/.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall core (\u0026lt;\u0026thinsp;250 acres)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal area of any landscape /Forest patches that are smaller than 250acres/.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium core (250\u0026ndash;500 acres)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal area of any landscape /Forest patches between 250 and 500 acres/.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge core (\u0026gt;\u0026thinsp;500 acres)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe internal area of patches after the elimination of edge buffer /Forest patches greater than 500 acres/.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Methods for ESV\u003cem\u003ef\u003c/em\u003e Valuation\u003c/h2\u003e \u003cp\u003eThe analysis for Ecosystems Service Value functions (ESV\u003cem\u003ef\u003c/em\u003e) of LULC types and fragmented categories were based on benefit transfer approach [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The coefficients of tropical areas and Central Asia on regional estimates of ESV\u003cem\u003ef\u003c/em\u003e using data provided by [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] were used. The ESV\u003cem\u003ef\u003c/em\u003e used in this study, therefore, were calculated using coefficients obtained through transfer approach from other research works [9; 25; 48] to investigate the effects of landscape fragmentation on ecosystem service functions of ESV\u003cem\u003ef\u003c/em\u003e of LULC types. The biomes used as proxies for the fragmented categories of LULC classes were based on Kreuter et al., [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e LULC categories and ecosystem service coefficients in US\u003cspan\u003e$\u003c/span\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ey\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e based on the modified estimates\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC categories and ecosystem service coefficients in US\u003cspan\u003e$\u003c/span\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ey\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e based on the modified estimates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTropical Forests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver/Lake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesert\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSwamps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVillages\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eProvisioning service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenetic resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eRegulating services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGas regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimatic regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisturbance regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaste treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e431.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErosion control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSupporting services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNutrient cycling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil formation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHabitat/refugia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCultural services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCulture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecreation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003cp\u003eThe equivalent value coefficients of each ES were used to calculate ecosystem service value functions. The values of individual ecosystem service value functions provided by LULC types and fragmented categories were calculated [26; 29] as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({ESV}_{fk}\\)\u003c/span\u003e \u003c/span\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum {(A}_{k}\\)\u003c/span\u003e\u003c/span\u003e *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({VC}_{k}\\)\u003c/span\u003e\u003c/span\u003e)\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;\u0026hellip;\u0026hellip;... (3)\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ESV}_{fk}\\)\u003c/span\u003e\u003c/span\u003eis estimated total ecosystem service value functions of LULC type \u003cem\u003eK\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{k}\\)\u003c/span\u003e\u003c/span\u003e is the area (ha) of LULC type \u003cem\u003eK\u003c/em\u003e, and \u003cem\u003eVC\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e the value coefficient of function (USD ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for each LULC type \u003cem\u003eK.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTo compute effects of landscape fragmentation on ESV\u003cem\u003ef\u003c/em\u003e of LULC types, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(ESVf\\)\u003c/span\u003e\u003c/span\u003eof LULC types and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(ESVf\\)\u003c/span\u003e\u003c/span\u003e of fragmented categories such as patch, edge, perforated, small core area, medium core area and large core area were estimated based on the coefficient of each biome as:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}\\text{E}\\text{S}\\text{V}f=({\\text{E}\\text{S}\\text{V}\\text{f}}_{\\text{p}}\\text{*} {\\text{V}\\text{C}}_{P}+{\\text{E}\\text{S}\\text{V}\\text{f}}_{e}*{{\\text{V}\\text{C}}_{e}+ \\text{E}\\text{S}\\text{V}\\text{f}}_{Pr}*{VC}_{Pr}{+\\text{E}\\text{S}\\text{V}\\text{f}}_{Sc}{* VC}_{SC}+{ESVf}_{mc}* {VC}_{mc }+{ESVf}_{lc}*{VC}_{lc})\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;.\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{E}\\text{S}\\text{V}\\text{f}\\)\u003c/span\u003e\u003c/span\u003e is the total ecosystem service values of fragmented categories and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ \\text{E}\\text{S}\\text{V}\\text{f}}_{\\text{p}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{E}\\text{S}\\text{V}\\text{f}}_{e},\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ESVf}_{Pr}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ ESVf}_{SC }\\)\u003c/span\u003e\u003c/span\u003e, and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ ESVf}_{LC}\\)\u003c/span\u003e\u003c/span\u003erepresent ecosystem service functions of patch, edge, perforated, small core area, medium core area and large core area respectively.\u003c/p\u003e \u003cp\u003eThe difference between estimated total ecosystem service value functions of LULC type K (Eq.\u0026nbsp;3) and ecosystem service values of fragmented categories (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were computed as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{E}\\text{S}\\text{V}f}_{ek }={ESVf}_{k}\\)\u003c/span\u003e \u003c/span\u003e -\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ESVf}_{C}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;...\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;...\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (5)\u003c/p\u003e \u003cp\u003eWhere,\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{E}\\text{S}\\text{V}f}_{ek }\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e=\u003c/em\u003e effect of fragmented categories on ecosystem service value functions of LULC type\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({ESVf}_{k}\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e=\u003c/em\u003e estimated total ecosystem service value function of LULC type K\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({ESVf}_{C}\\)\u003c/span\u003e \u003c/span\u003e = estimated total ecosystem service value function of fragmented categories\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Analysis of Sensitivity for Ecosystem Services\u003c/h2\u003e \u003cp\u003eThe method for determining the value of ecosystem services was developed by multiplying the area of a given land use category by the relevant value coefficient. However, due to the unpredictable, dynamic, and nonlinear nature of social-ecological systems, the results obtained using Benefit Transfer Approach (BTA) has low resolution, large deviation, and significant uncertainty [30; 50; 57]. Therefore, coefficients the proxies' biomes were used to quantify ecosystem service functions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although the biomes employed as proxies, land use might be used as a substitute for ecosystem services. Therefore, the value coefficients were adjusted by \u0026plusmn;\u0026thinsp;50% using sensitivity analysis (26). By adjusting the ESV coefficient per unit area of each land use type by \u0026plusmn;\u0026thinsp;50.0%, the CSs of ESV of landscapes can be calculated to maintain the reliability of ecosystem service valuation as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\begin{array}{cc}CS =\u0026amp; \\frac{{(ESV}_{j }-{ESV}_{i} /{ESV}_{i}}{{VC}_{jk}-{VC}_{ik }/{VC}_{ik}}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e \u003cp\u003eWhere CS\u0026thinsp;=\u0026thinsp;coefficient of sensitivity, ESV is estimated ESV, VC\u0026thinsp;=\u0026thinsp;value coefficient, i and j are initial and adjusted value. If value is CS\u0026thinsp;\u0026le;\u0026thinsp;1, then ESV considered inelastic and when CS\u0026thinsp;\u0026ge;\u0026thinsp;1 is considered elastic relative to value coefficient (Q. Zhao et al., 2020).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results AND Discussions","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.1 Distribution and dynamics of LULC change\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan\u003e2\u003c/span\u003e shows the spatial representation of LULC types of Boma-\u003cstrong\u003eG\u003c/strong\u003eambella Trans-boundary Landscap\u003cstrong\u003ee\u003c/strong\u003e. The area of each LULC types, percentage share and annual rate of change were extracted from Landsat images (2009 \u0026amp; 2020). The result shows six LULC types: forest land, agricultural land, water bodies, wetland, bare land and settlement. It has been observed that the landscape was dominated by forest land covering 52.3% of the total area of the landscape in 2009 followed by agricultural land (36%), bare land (7.7%) and wetland (3.7%). The forest land that was 52.3% in 2009 has been reduced to 45.1% in 2020 with an annual rate of decreasing change 40.35% (18247.1 ha/yr).\u003c/p\u003e\n \u003cp\u003eTable \u003cspan\u003e4\u003c/span\u003e LULC Types, Area and percent Share and annual rate of change for Boma-Gambella trans-boundary landscape (2009\u0026ndash;2020)\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLULC Types, Area and percent Share and annual rate of change for Boma-Gambella trans-boundary landscape (2009\u0026ndash;2020)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC Types\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAnnual rate of LULC change (ha/yr)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAnnual rate of LULC change (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (Ha.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (Ha.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForest Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,460,133.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,259,416.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18247.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgricultural land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,004,378.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,233,877.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20863.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8910.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,123.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-253.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208,246.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186,370.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1988.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWet land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104,491.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97,172.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-665.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,380.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,581.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,789,540.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,789,540.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eSource: ArcGIS 10.7 (2022)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e\n \u003cp\u003eMoreover, water bodies, bare land and wetlands have been experienced a decreasing annual rate of change, while agricultural land that was 36% in 2009 has been increased to 44.2% in 2020 with an annual rate of increasing change (20863.6 ha/yr), followed by settlement areas with an annual rate of increasing change (291.0 ha/yr) between 2009 \u0026amp; 2020 (Table \u003cspan\u003e4\u003c/span\u003e). The highest annual rate of decreasing change for forest land has been due to expansion agricultural land and settlement areas.\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e\n \u003cp\u003eSource: ArcGIS 10.7 (2022)\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e2\u003c/span\u003e LULC Change maps for 2009 and 2020\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.2 Land-Use and Land-Cover Change Transition Matrix\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan\u003e5\u003c/span\u003e shows the cross-tabulation LULC change matrix from 2009 to 2020. The result revealed that forest land, water bodies, and wetland experienced an annual decreasing rate of conversion, while, agricultural land, bare land and sentiment areas showed an annual increasing rate of class change. Forest land showed the largest annual decreasing rate conversion (124,178.5ha/yr), while, agricultural land revealed the highest increasing rate of conversion (43,424.6ha/year).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLand use/Land cover change transition matrix\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eLand use land cover types in 2009\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eLand use land cover types in 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC Classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForest land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgricultural land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWetland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForest land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63,293.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,499.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3377.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2054.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgricultural land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70,401.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e729.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201,874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,424.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e582.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e937.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e317.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,209.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46,435.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30,157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWetland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,688.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,263.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2321.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,007.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1288.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,144.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e432.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94,170.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130,073.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,007.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204,945.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28,821.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38,336.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAnnual Rate of Conversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20321.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17902.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9451.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188002.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19987.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37738.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eSource: ArcGIS (2022)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan\u003e5\u003c/span\u003e Land use/Land cover change transition matrix\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.3 Analysis of Landscape Fragmentation\u003c/h2\u003e\n \u003cp\u003eLandscape fragmentation categories were extracted using ARC-GIS 10.7 and landscape fragmentation categories were calculated using FRAGSTATS 4.2. Accordingly, the largest fragmented land use type was forest land, accounting 2177368.02ha and 1506759.2ha in 2009 and 2020 respectively, while agricultural land was the second largest fragmented landscape, accounting 101925.3ha in 2009 and 245145.49 ha in 2020 (Table \u003cspan\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eArea in hectare for each fragmented class (2009 \u0026amp;2020)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFragmented Classes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eForest land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgricultural land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWetland\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35544.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17773.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53342.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102194.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51297.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerforated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65291.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32853.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026lt;\u0026thinsp;250 acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19693.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(250-500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4622.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4622.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026gt;\u0026thinsp;500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1950020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1950020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2177368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101925.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2279530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88072.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44539.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e968.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188548.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49738.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1219.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerforated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301438.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150868.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e609.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026lt;\u0026thinsp;250 acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39802.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39802.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(250-500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11371.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11371.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026gt;\u0026thinsp;500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e877525.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e877526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1506759.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245145.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2798.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1755088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSource: ArcGIS 10.7 and FRAGSTAT 4.2 (2022)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan\u003e6\u003c/span\u003e Area in hectare for each fragmented class (2009 \u0026amp;2020)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e3\u003c/span\u003e. Fragmentation categories of Boma-Gambella landscape (2009\u0026ndash;2020)\u003c/p\u003e\n \u003cp\u003eTable \u003cspan\u003e7\u003c/span\u003e also shows the fragmented categories, percent share and annual rate of change for Boma-Gambella Trans-boundary Landscape (2009\u0026ndash;2020). The result revealed that the core area (\u0026gt;\u0026thinsp;500 acres) has been the dominant class (81.7% \u0026amp; 52.4%) in 2009 and 2020 respectively.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLandscape Fragmentation change (2009\u0026ndash;2020)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFragmented Classes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (ha) 2009\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (ha) 2020\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChange Area (ha) 2009\u0026ndash;2020\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChange Area (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnual rate of change (ha/yr)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53342.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80338.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7303.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7824.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerforated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15128.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026lt;\u0026thinsp;250 acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39802.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20108.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(250-500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4622.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11371.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6748.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e613.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026gt;\u0026thinsp;500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1950020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e877526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1072494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-97499.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2388189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1675376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-712813.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-64801.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eSource: ArcGIS 10. 7 and FRAGSTAT 4.2 (2022)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan\u003e7\u003c/span\u003e Landscape Fragmentation change (2009\u0026ndash;2020)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e\n \u003cp\u003eThe perforated area has been the second dominant fragmented class (8.7% \u0026amp; 22.3%) in 2009 and 2020 respectively. In 2009, large core area occupied 1950020 ha of land, while in 2020 it was significantly reduced to 877526ha. Large core area lost 1072494 ha within these eleven-years with annual rate of decreasing change (97499.5 ha/yr). In 2009, the perforated area was 206926ha and increased dramatically in 2020 to 373338ha with annual rate of increasing change (15128.4ha/yr), thereby degrading the perforated areas into more edges area. The edge area in 2009 was 153584ha and increased slightly in 2020 to 239657ha with annual rate of increasing change (7824.8ha/yr). Perforated area shows an increase of 12.2% from 8.7% in 2009 to 22.3% in 2020. The increase in the perforated area shows that more of the core areas of the landscape has been degraded continuously and converted in perforated areas. Patch area 53342.2ha in 2009 has been transformed in to 133681 ha in 2020, with an increase of 5.3% from 2.3% in 2009 7.6% in 2020 and the overall change shows that landscape fragmentation has been at the core areas of the forest land and large areas of the forest land were more fragmented compared with other LULC types.\u003c/p\u003e\n \u003cp\u003eThe transformation of 2.3% of patch area in 2009 to 8% in 2020; 6.4% edge area in 2009 to 14.4% in 2020 and 8.7% of perforated area in 2009 to 22.3% in 2020 indicate the fragmentation of core areas of the landscape. The highest rate of fragmentation has been observed in large core area (\u0026gt;\u0026thinsp;500acres) of the forest land between 2009 and 2020 at a decreasing change rate of 35.5%. This highest rate fragmentation of core area has been due to the increasing change in perforated areas, edge areas, and patch areas in the study landscape.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.4 Ecosystem Services Values of functions (ESV\u003cem\u003ef\u003c/em\u003e) of Fragmented Categories\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan\u003e8\u003c/span\u003e demonstrates the ecosystem service value functions (ESV\u003cem\u003ef\u003c/em\u003e) of fragmented categories LULC types in the Boma-Gambella landscape. The results reveal that the landscape has total ESV\u003cem\u003ef\u003c/em\u003e of 2173.2 million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1545.6 million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2009 and 2020 respectively.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 8\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLandscape fragmentation Categories and estimated ESV\u003cem\u003ef (\u003c/em\u003eMillion \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFragmented Classes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eForest land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgricultural land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWetland\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerforated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026lt;\u0026thinsp;250 acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(250-500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026gt;\u0026thinsp;500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1924.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1924.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2148.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2173.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerforated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e332.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026lt;\u0026thinsp;250 acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(250-500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026gt;\u0026thinsp;500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e865.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e865.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1486.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1545.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eChange (2009\u0026ndash;2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerforated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026lt;\u0026thinsp;250 acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(250-500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore(\u0026gt;\u0026thinsp;500acres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1058.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1058.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-661.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-627.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSource: Computed based on ArcGIS 10. 7, FRAGSTAT 4.2 and BTA (2022)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan\u003e8\u003c/span\u003e Landscape fragmentation Categories and estimated ESV\u003cem\u003ef (\u003c/em\u003eMillion \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e\n \u003cp\u003eAmong LULC types,, forest cover fragmented categories provide the greatest ESV\u003cem\u003ef\u003c/em\u003e (2148.4 million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and (1486.7 million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in 2009 and 2020 respectively, followed by fragmented categories of agricultural land, 23 million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2009 and 55.3 million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2020, while wetland accounts the third largest provider of ecosystem service value functions (1.7 million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eand 2.7 million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in 2009 and 2020 respectively. The results also show that the core areas the forest land has highest ESV\u003cem\u003ef\u003c/em\u003e, 1948.1 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2009 and 916.3 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2020.\u003c/p\u003e\n \u003cp\u003eThe study also depicts the ESV\u003cem\u003ef\u003c/em\u003e change between 2009 and 2020 in Boma-Gambella Trans-boundary landscape. The results reveal that there were a total decrease of 627.65 \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in ESV\u003cem\u003ef\u003c/em\u003e from 2009 to 2020, with the highest decreasing change accounted for by core areas (\u0026gt;\u0026thinsp;500 acres) of forest land (1058.30 million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The results therefore, confirmed that landscape fragmentation in Boma-Gambella Trans-boundary landscape has significantly changed the ESV\u003cem\u003ef\u003c/em\u003e of the landscape from 2009 to 2020.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.5 Spatial Variations and Temporal Changes of Ecosystem Service Value Functions (ESV\u003cem\u003ef)\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan\u003e9\u003c/span\u003e demonstrates the ecosystem service value functions change at temporal and spatial scale in Boma-Gambella Trans- Boundary Landscape. The study results show that there were spatial variations in ESV\u003cem\u003ef\u003c/em\u003e in the last 11 years. Accordingly, forest land and agricultural areas have the greatest regulating, provisioning, supporting and cultural service value functions. The water bodies and wetlands provide minimal service value functions as compared with forest land and agricultural land areas.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 9\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSpatial Variations and Temporal Changes in ESV\u003cem\u003ef\u003c/em\u003e (Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eESV Functions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eForest land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgriculture/crop land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWetland\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvisioning service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulating services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e928.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e638.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1573.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupporting services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultural services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e991.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2442.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvisioning service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e331.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulating services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e784.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1590.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupporting services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e529.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultural services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1242.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1217.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2469.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eNet Change (2009\u0026ndash;2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvisioning service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulating services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-127.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupporting services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-42.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-48.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-90.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultural services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-198.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e394.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAnnual rate of Change (2009\u0026ndash;2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvisioning service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulating services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupporting services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultural services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSource: Computed based on ArcGIS 10. 7, FRAGSTAT 4.2 and BTA (2022)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan\u003e9\u003c/span\u003e Spatial Variations and Temporal Changes in ESV\u003cem\u003ef\u003c/em\u003e (Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/p\u003e\n \u003cp\u003eMoreover, ecosystem service value functions of the landscape change at temporal scales. The results of the study reveal that regulating, provisioning, supporting and cultural service value functions of the forest land decreased at a net change of 198.05 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with annual rate of decreasing change (18.0 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) from 2009 t0 2020. The results of the study also reveal that regulating service value functions of the forest land significantly decreased with a net change of 127.65 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at annual decreasing rate of change (11.6 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) from 2009 to 2020, indicating a higher susceptibility of the forest land to external interference. Moreover, the results show that regulating, provisioning and supporting service value functions of water bodies decreased with a net change of 2.75 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at annual decreasing change rate of 0.25 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e from 2009 to 2020. Accordingly, gas regulation, climate regulation, and hydrological regulation services reduced due to the decrease in forest land and water bodies. The study results also confirm that supporting services value functions of forest land and agricultural land decreased by 42.49 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 48.58 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eat annual decreasing rate of 3.9 and 4.4 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003efrom 2009 t0 2020 respectively. These results suggest that nutrient cycling, soil formation and habitat/refugia service value functions of forest land and agricultural land were significantly reduced due to agricultural investment projects, settlement expansion and population encroachments in the study landscape.\u003c/p\u003e\n \u003cp\u003eAlthough ESV\u003cem\u003ef\u003c/em\u003e from natural land cover has decreased in the present study area due to landscape fragmentation, there has been an increase in ESV\u003cem\u003ef\u003c/em\u003e from agricultural land investment. The results show that ESV\u003cem\u003ef\u003c/em\u003e of agricultural land increased from 991.01 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2009 to 1217.45 Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2020. As confirmed by most FGDs and kIIs, however, much of the services from agricultural land investment have been left for export earnings for governments, foreign and local investors, with little or no benefit for local communities.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.6 Coefficient of Sensitivity\u003c/h2\u003e\n \u003cp\u003eThe coefficient of sensitivity (CS) analysis in this study for LULC change, landscape fragmentation and ecosystem service function change study (2009\u0026ndash;2020) were all less than \u003cspan type=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;1. The sensitivity of forest land, and wetlands were larger with sensitivity coefficient\u0026thinsp;\u0026lt;\u0026thinsp;1.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan\u003e10\u003c/span\u003e Coefficient of sensitivity for ecosystem service functions (ESV\u003cem\u003ef\u003c/em\u003e) in Boma-Gambella landscape (2009 \u0026amp; 2020)\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 10\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCoefficient of sensitivity for ecosystem service functions (ESV\u003cem\u003ef\u003c/em\u003e) in Boma-Gambella landscape (2009 \u0026amp; 2020)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eChange in Valuation Coefficient (VC)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eThe Effect of Changing VC by \u0026plusmn; 50% from the Original Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForest Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgricultural land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWet land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe sensitivity coefficient of forest cover was highest, 1- 0.8 for ESV\u003cem\u003ef\u003c/em\u003e assessment, this indicated that when the forest ESV\u003cem\u003ef\u003c/em\u003e coefficient increased or decreased by 1%, the total ESV\u003cem\u003ef\u003c/em\u003e increased or decreased by 1%- 0.8% for 2009\u0026ndash;2020 (Table \u003cspan\u003e10\u003c/span\u003e). The sensitivity analysis\u0026apos;s findings, therefore, indicated that the ecosystem service function (ESV\u003cem\u003ef)\u003c/em\u003e estimation for the study area were rather inelastic with respect to the value coefficients, which implies that the estimation of ESV\u003cem\u003ef\u003c/em\u003e were reliable and trustworthy, which is in line with other studies [25; 25;55; 61].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe study based on spatial and temporal changes of ecosystem service value functions in response to the effects of landscape fragmentation in Boma-Gambella Trans boundary Landscape, Southwest Ethiopia. The findings of the study, assert a significant transformation of LULC types and fragmentation of landscape categories. Accordingly, ecosystem service value functions change at temporal scales, from 2009 to 2020 due to fragmentation of the landscapes in to core, perforated, edge, and patch areas and vary across LULC types. The study findings are, therefore, in confirmation with [43; 60] that landscape fragmentation is widely known as the principal causes of biodiversity depletion and loss of ESV\u003cem\u003ef\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe analysis reveals a decline in ecological land from 2009 to 2020 and significant changes in landscape patterns. Anthropogenic factors, such as agricultural investment and settlement expansion have remained driver of land use land cover transformation and landscape fragmentation and result in spatial variations and temporal changes in ecosystem service functions. The study highlights the vital importance of understanding how fragmentation of natural land cover affects ecosystem service functions. Incorporating these effects into ecosystem service assessments is critical to the development of effective tools that can help landscapes to provide multiple ecosystem services. Therefore, the need for empirical research into the exact nature of the relationships between fragmentation and ecosystem service functions is critical. Moreover, as the ecosystem services concept is increasingly incorporated into decision-making and planning activities, the need to improve understanding of ecosystem service function at the landscape scale is fundamentally important. Thus, for addressing landscape fragmentation effect on ecosystem service functions, a high-resolution satellite images, detailed field survey and a more integrated analysis is essential for sustainable utilization of the landscape resources. The information generated in this study will, therefore, be crucial in developing viable policies, strategies and programs to promote sustainable use of landscapes in similar biophysical and socio-economic settings as Boma-Gambella Trans-boundary Landscape, Southwest Ethiopia and East South Sudan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Competing Interest:\u003c/strong\u003e The authors declare that we have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreDiT Authorships Taxonomy:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eAzemir Berhanu Getahun\u003cem\u003e:\u003c/em\u003e\u0026nbsp; Writing- original draft and formal analysis; Amare\u003cem\u003e\u0026nbsp;\u003c/em\u003eBantider Dagnew: Conceptualization, Methodology, Visualization and Investigation.\u0026nbsp;Desalegn Yayeh Ayal:\u0026nbsp;Supervision,\u0026nbsp;Writing-Reviewing, Editing and Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003cem\u003e:\u003c/em\u003e\u003c/strong\u003e Data will be made available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003cem\u003es\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u003c/em\u003e The authors are grateful to HoAREC\u0026amp;N and Addis Ababa University (AAU) for financial support for data collection for the first author of this paper.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBenjamin Grey, Cummings Colin \u0026amp; Evangelides Ellen. Exploring the Effects of Land Use and Land Cover Change on White-Eared Kob Migration in the Gambella Region, Ethiopia. Environmental Policy. 2013.Missing Players in Environmental Governance. \u003c/li\u003e\n\u003cli\u003eBennett, E. M., Cramer, W., Begossi, A., Cundill, G., D\u0026iacute;az, S., Egoh, B. N. Woodward, G. Linking biodiversity, ecosystem services, and human well-being: three challenges for designing research for sustainability. Current Opinion in Environmental Sustainability. 2015; 14, 76\u0026ndash;85. doi:10.1016/j.cosust.2015.03.007 \u003c/li\u003e\n\u003cli\u003eBindajam, A. A., Mallick, J., Mahato, S., Talukdar, S., \u0026amp; Alquadhi, S. Assessing landscape fragmentation effects on ecosystem services in a semi-arid mountainous environment: A case study on abha watershed, saudi arabia. \u003cem\u003eApplied Ecology and Environmental Research\u003c/em\u003e. 2021; \u003cem\u003e19\u003c/em\u003e(3), 2519\u0026ndash;2539. https://doi.org/10.15666/aeer/1903_25192539\u003c/li\u003e\n\u003cli\u003eBirdLife International. Important Bird Area factsheet: Gambella National Park, Ethiopia. 2009. http://www.birdlife.org. \u003c/li\u003e\n\u003cli\u003eBoulanger, Yan \u0026amp; Pascual, Jesus. Boreal forests will be more severely affected by projected anthropogenic climate forcing than mixed wood and northern hardwood forests in eastern Canada. Landscape Ecology. 2021; 36. 10.1007/s10980-021-01241-7.\u003c/li\u003e\n\u003cli\u003eChen, Wanxu \u0026amp; Chi, Guangqing \u0026amp; Li, Jiangfeng.. Ecosystem Services and Their Driving Forces in the Middle Reaches of the Yangtze River Urban Agglomerations, China. International Journal of Environmental Research and Public Health. 2020; 17. 3717. 10.3390/ijerph17103717.\u003c/li\u003e\n\u003cli\u003eChristine F, Susanne F, Anke W, Lars K, Franz M. Assessment of the effects of forestland use strategies on the provision of ecosystem services at regional scale. Journal of Environmental Management. 2013; 127: 96\u0026ndash;116.\u003c/li\u003e\n\u003cli\u003eClerici, N., Paracchini, M. L., \u0026amp; Maes, J. Land-cover change dynamics and insights into ecosystem services in European stream riparian zones. Ecohydrology \u0026amp; Hydrobiology. 2014; 14(2), 107\u0026ndash;120. doi:10.1016/j.ecohyd.2014.01.002 \u003c/li\u003e\n\u003cli\u003eCoates, D., Pert, P. L., Barron, J., Muthuri, C., Nguyen-Khoa, S., Boelee, E., \u0026amp; Jarvis, D. I. Water-related ecosystem services and food security. \u003cem\u003eManaging Water and Agroecosystems for Food Security\u003c/em\u003e. 2013; 29\u0026ndash;41. https://doi.org/10.1079/9781780640884.00\u003c/li\u003e\n\u003cli\u003eColby Environmental Policy Group. Food, Forests, and Livelihoods in East Africa. Waterville, Maine: Colby College Environmental Studies Program. 2015.\u003c/li\u003e\n\u003cli\u003eCostanza, R., d\u0026apos;Arge, R., de Groot, R. The value of the world\u0026apos;s ecosystem services and natural capital. 1997; Nature 387. https://doi.org/10.1038/387253a0\u003c/li\u003e\n\u003cli\u003eCostanza, R., de Groot, R., Sutton, P., van der Ploeg, S., Anderson, S. J., Kubiszewski, I., Farber, S., \u0026amp; Turner, R. K. Changes in the global value of ecosystem services. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e. 2014; \u003cem\u003e26\u003c/em\u003e(1), 152\u0026ndash;158. https://doi.org/10.1016/j.gloenvcha.2014.04.002\u003c/li\u003e\n\u003cli\u003eCowling, R.M., Egoh, B., Knight, A.T., O\u0026rsquo;Farrel, P., Reyers, j., Rouget\u0026rsquo;ll, B., Roux, M., Weiz, D.J., Wilhelm-Rechman, A.A. An operational model for mainstreaming ecosystem services for implementation. Proceedings of the National Academy of Science. 2008; 105, 9483\u0026ndash;9488.\u003c/li\u003e\n\u003cli\u003eDeFries R S, Foley J A and Asner G P. Land-use choices: balancing human needs and ecosystem function Frontiers. Ecol. Environ. 2004; 2 249\u0026ndash;57.\u003c/li\u003e\n\u003cli\u003eDi Baldassarre, G., Elshamy, M., van Griensven, A., Soliman, E., Kigobe, M., Ndomba, P. Uhlenbrook, S. Future hydrology and climate in the River Nile basin: a review. Hydrological Sciences Journal. 2011; 56(2), 199\u0026ndash;211. doi:10.1080/02626667.2011.557378.\u003c/li\u003e\n\u003cli\u003eEcheverria C, Coomes D, Salas J, Rey-Benayas JM, Lara A, Newton A. Rapid deforestation and fragmentation of Chilean temperate forests. Biol Conserv. 2006; 130:481\u0026ndash;494\u003c/li\u003e\n\u003cli\u003eEwers RM, Didham RK. Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews. 2006; 81(1):117-142. doi:10.1017/S1464793105006949\u003c/li\u003e\n\u003cli\u003eFahad, K. H., Hussein, S., \u0026amp; Dibs, H. Spatial-Temporal Analysis of Land Use and Land Cover Change Detection Using Remote Sensing and GIS Techniques. \u003cem\u003eIOP Conference Series: Materials Science and Engineering. \u003c/em\u003e2020; \u003cem\u003e671\u003c/em\u003e(1). https://doi.org/10.1088/1757-899X/671/1/012046\u003c/li\u003e\n\u003cli\u003eFahrig, Lenore. Ecological Responses to Habitat Fragmentation Per Se. Annual Review of Ecology, Evolution, and Systematics. 2017; 48. 10.1146/annurev-ecolsys-110316-022612\u003c/li\u003e\n\u003cli\u003eFisher, B., Turner, R.K. Ecosystem services: classification for valuation. Biological Conservation. 2008; 141, 1167\u0026ndash;1169.\u003c/li\u003e\n\u003cli\u003eFoley, J., Ramankutty, N., Brauman, K. et al. Solutions for a cultivated planet. Nature. 2011; 478, 337\u0026ndash;342. https://doi.org/10.1038/nature10452\u003c/li\u003e\n\u003cli\u003eGeist, H.J., Lambin, E.F. Proximate causes and underlying driving forces of tropical deforestation. Bioscience. 2002; 52, 143\u0026ndash;150.\u003c/li\u003e\n\u003cli\u003eGerland, Patrick \u0026amp; Raftery, Adrian \u0026amp; \u0026Scaron;evč\u0026iacute;kov\u0026aacute;, Hana \u0026amp; Li, Nan \u0026amp; Gu, Danan \u0026amp; Spoorenberg, Thomas \u0026amp; Alkema, Leontine \u0026amp; Fosdick, Bailey \u0026amp; Chunn, Jennifer \u0026amp; Lalic, Nevena \u0026amp; Bay, Guiomar \u0026amp; Buettner, Thomas \u0026amp; Heilig, Gerhard \u0026amp; Wilmoth, John. World Population Stabilization Unlikely This Century. Science. 2014; 10.1126/science.1257469. \u003c/li\u003e\n\u003cli\u003eGreen, K. Change Metters, Photogrammetric Engineering and Remote Sensing. 2011; 77:4, 305:309.\u003c/li\u003e\n\u003cli\u003eKindu, M., Schneider, T., Teketay, D., \u0026amp; Knoke, T. Science of the Total Environment Changes of ecosystem service values in response to land use / land cover dynamics in Munessa \u0026ndash; Shashemene landscape of the Ethiopian highlands. \u003cem\u003eScience of the Total Environment. \u003c/em\u003e2016; \u003cem\u003e547\u003c/em\u003e, 137\u0026ndash;147. https://doi.org/10.1016/j.scitotenv.2015.12.127\u003c/li\u003e\n\u003cli\u003eKreuter, U. P., Harris, H. G., Matlock, M. D., \u0026amp; Lacey, R. E. Change in ecosystem service values in the san antonio area, Texas. \u003cem\u003eEcological Economics\u003c/em\u003e. 2001; \u003cem\u003e39\u003c/em\u003e(3), 333\u0026ndash;346. https://doi.org/10.1016/S0921-8009(01)00250-6\u003c/li\u003e\n\u003cli\u003eKussia, A., Tolossa, D., \u0026amp; Abate, E. A Crisis of the Green Gold: A Case Study of Large Scale Agricultural Investment in Gambella Region, Ethiopia. Eastern Africa Social Science Research Review. 2020; 36(2), 53\u0026ndash;94. doi:10.1353/eas.2020.0006 \u003c/li\u003e\n\u003cli\u003eLambin, E.F. \u0026amp; Turner, Brittney \u0026amp; Geist, H.J. \u0026amp; Agbola, Samuel \u0026amp; Angelsen, Arild \u0026amp; Bruce, J.W. \u0026amp; Coomes, Oliver \u0026amp; Dirzo, R. \u0026amp; Fischer, G\u0026uuml;nther \u0026amp; Folke, Carl \u0026amp; George, P.S. \u0026amp; Homewood, K. \u0026amp; Imbernon, J. \u0026amp; Leemans, Rik \u0026amp; Li, Xiubin \u0026amp; Moran, Emilio \u0026amp; Mortimore, M. \u0026amp; Ramakrishnan, P.S. \u0026amp; Richards, J.F. \u0026amp; Xu, Jianchu. The causes of land-use and land-cover change: Moving beyond the myths. Global Environmental Change-Human and Policy Dimensions. 2001; 11 (2001). - ISSN 0959-3780. 11.\u003c/li\u003e\n\u003cli\u003eLi, J., Chen, H., Zhang, C., \u0026amp; Pan, T. Variations in ecosystem service value in response to land use/land cover changes in Central Asia from 1995-2035. \u003cem\u003ePeerJ\u003c/em\u003e, 2019; \u003cem\u003e2019\u003c/em\u003e(9), 1\u0026ndash;22. https://doi.org/10.7717/peerj.7665\u003c/li\u003e\n\u003cli\u003eLimburg, K. E., O\u0026rsquo;neill, R. V., Costanza, R., \u0026amp; Farber, S. Complex systems and valuation. \u003cem\u003eEcological Economics\u003c/em\u003e. 2002;\u003cem\u003e 41\u003c/em\u003e(3), 409\u0026ndash;420. https://doi.org/10.1016/S0921-8009(02)00090-3\u003c/li\u003e\n\u003cli\u003eLin Yang, Feixue Shen, Lei Zhang, Yanyan Cai, Fangxin Yi, Chenghu Zhou.. Quantifying influences of natural and anthropogenic factors on vegetation changes using structural equation modeling: A case study in Jiangsu Province, China, Journal of Cleaner Production. 2021; Volume 280, Part 2, 124330, ISSN 0959 6526. https://doi.org/10.1016/j.jclepro.2020.124330.\u003c/li\u003e\n\u003cli\u003eLiu, Jiajia \u0026amp; Wilson, Maxwell \u0026amp; Hu, Guang \u0026amp; Liu, Jinliang \u0026amp; Wu, Jianguo \u0026amp; Yu, Mingjian. How does habitat fragmentation affect the biodiversity and ecosystem functioning relationship? Landscape Ecology. 2018; 33. 10.1007/s10980-018-0620-5.\u003c/li\u003e\n\u003cli\u003eLu, D., Mausel, P., Brondizio, E. and Moran, E. Change detection techniques, International Journal of Remote Sensing. 2004; 25:12, 2365-2401.\u003c/li\u003e\n\u003cli\u003eMacLean, M.G., \u0026amp; Congalton, R. G., \u0026ldquo;Mapping and analysis of fragmentation in South eastern New Hampshire\u0026rdquo;, A special joint symposium of ISPRS technical commission iv \u0026amp; Auto Carto in conjunction with ASPRS/CaGIS. 2010; Fall Specialty Conference, Orlando, Florida.\u003c/li\u003e\n\u003cli\u003eMahmoud, Shereif \u0026amp; Gan, Thian Yew. Impact of anthropogenic climate change and human activities on environment and ecosystem services in arid regions. Science of the Total Environment. 2018; 633. 1329-1344. 10.1016/j.scitotenv.2018.03.290.\u003c/li\u003e\n\u003cli\u003eMas, J.F. Monitoring land-cover changes: a comparison of change detection techniques, International Journal of Remote Sensing. 1999; 20:1, 139-152.\u003c/li\u003e\n\u003cli\u003eMcDonald, R.I. Ecosystem Service demand and supply along the urban-to-rural gradient. Journal of Conservation Planning. 2009; 5, 1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eMiranda A, Altamirano A, Cayuela L, Pincheira F, Lara A. Different times, same story: native forest loss and landscape homogenization in three physiographical areas of south-central of Chile. Appl Geogr. 2015; 60:20\u0026ndash;28. doi: 10.1016/j.apgeog.2015.02.016. \u003c/li\u003e\n\u003cli\u003eMishra, K., Sam, A. G., Miranda, M. J., \u0026amp; Diiro, G. M. Gender and Dynamics of Technology Adoption: Evidence from Uganda. \u003cem\u003eAgricultural \u0026amp; Applied Economics Association and Western Agricultural Economics Association. 2015; Annual Meeting\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eMitchell, Matthew \u0026amp; Su\u0026aacute;rez Castro, Andr\u0026eacute;s \u0026amp; Martinez-Harms, Maria \u0026amp; Maron, Martine \u0026amp; Mcalpine, Clive \u0026amp; Gaston, Kevin \u0026amp; Johansen, Kasper \u0026amp; Rhodes, Jonathan.. Reframing landscape fragmentation\u0026apos;s effects on ecosystem services. Trends in Ecology \u0026amp; Evolution. 2015; 30. 10.1016/j.tree.2015.01.011.\u003c/li\u003e\n\u003cli\u003eMohammed, S. L., \u0026amp; Behailu, T. \u003cem\u003eImpacts of human activities on wildlife: The case of Nile Lechwe (Kobus megaceros) Gambella National Park, Southwest Ethiopia. International Journal of Biodiversity and Conservation. \u003c/em\u003e2019; \u003cem\u003e11(1), 48\u0026ndash;57.\u003c/em\u003e doi:10.5897/ijbc2017.1144 \u003c/li\u003e\n\u003cli\u003eNaidoo, R., Balmford, A., Costanza, R., Fisher, B., Green, R.E., Lehner, B., Malcom, T.R., Ricketts, T.H. Global mapping of ecosystem services and conservation priorities. Proceedings of the National Academy of Science. 2008; 105, 9495\u0026ndash;9500.\u003c/li\u003e\n\u003cli\u003eNyssen, J.; Poesen, J.; Moeyersons, J. Deckers, J. Haile, M. and Lang A. Human impact on the environment in the Ethiopian and Eritrean highlands-a state of the art, Earth-Science Reviews. 2004; vol. 64, no. 3-4. \u003c/li\u003e\n\u003cli\u003eOwen‐Smith, N., Hopcraft, G., Morrison, T., Chamaill\u0026eacute;‐Jammes, S., Hetem, R., Bennitt, E., \u0026amp; Van Langevelde, F. Movement ecology of large herbivores in African savannas: current knowledge and gaps. \u003cem\u003eMammal review\u003c/em\u003e. 2020; \u003cem\u003e50\u003c/em\u003e(3), 252-266.\u003c/li\u003e\n\u003cli\u003ePal, S., Singha, P., Lepcha, K., Debanshi, S., Talukdar, S., \u0026amp; Saha, T. K.. \u003cem\u003eProposing multicriteria decision based valuation of ecosystem services for fragmented landscape in mountainous environment. Remote Sensing Applications: Society and Environment. \u003c/em\u003e2020; \u003cem\u003e100454.\u003c/em\u003e doi:10.1016/j.rsase.2020.100454\u003c/li\u003e\n\u003cli\u003ePhoomirat, R., Disyatat, N.R., Park, T.Y. . Rapid assessment checklist for green roof ecosystem services in Bangkok, Thailand. Ecol Process 9. 2020; https://doi.org/10.1186/s13717-020-00222-z.\u003c/li\u003e\n\u003cli\u003eShapiro, A.C., Aguilar-Amuchastegui, N., Hostert, P. Using fragmentation to assess degradation of forest edges in Democratic Republic of Congo. Carbon Balance Manage. 2016; 11. https://doi.org/10.1186/s13021-016-0054-9\u003c/li\u003e\n\u003cli\u003eShiferaw, H., Alamirew, T., Kassawmar, T., \u0026amp; Zeleke, G. Evaluating ecosystems services values due to land use transformation in the Gojeb watershed, Southwest Ethiopia. \u003cem\u003eEnvironmental Systems Research\u003c/em\u003e. 2021; \u003cem\u003e10\u003c/em\u003e(1). https://doi.org/10.1186/s40068-021-00227-3.\u003c/li\u003e\n\u003cli\u003eSilvestri, S., Zaibet, L., Said, M. Y., \u0026amp; Kifugo, S. C. Valuing ecosystem services for conservation and development purposes: A case study from Kenya. Environmental Science \u0026amp; Policy. 2013; 31, 23\u0026ndash;33. doi:10.1016/j.envsci.2013.03.008 \u003c/li\u003e\n\u003cli\u003eSolomon, N., Segnon, A. C., \u0026amp; Birhane, E. Ecosystem service values changes in response to land-use/land-cover dynamics in dry afromontane forest in northern Ethiopia. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e. 2019; \u003cem\u003e16\u003c/em\u003e(23), 7\u0026ndash;11. https://doi.org/10.3390/ijerph16234653\u003c/li\u003e\n\u003cli\u003eSu CH, Fu BJ. Evolutions of ecosystem services in the Chinese Loess Plateau under climatic and land use changes. Global and Planetary Change. 2013. 101:119\u0026ndash;128.\u003c/li\u003e\n\u003cli\u003eTerfa, B. K., Chen, N., Liu, D., Zhang, X., \u0026amp; Niyogi, D. Urban expansion in Ethiopia from 1987 to 2017: Characteristics, spatial patterns, and driving forces. \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e. 2019; 11(10), 1\u0026ndash;21. https://doi.org/10.3390/su11102973\u003c/li\u003e\n\u003cli\u003eTesfaye, K., Gbegbelegbe, S., Cairns, J., Shiferaw, B., Prasanna, B., Sonder, K., Boote, K., Makumbi, D., Robertson, R. Maize systems under climate change in sub-Saharan Africa. International Journal of Climate Change Strategies and Management. 2015; 7. 247-271. 10.1108/IJCCSM-01-2014-0005. \u003c/li\u003e\n\u003cli\u003eTian, C., Cheng, Ll. \u0026amp; Yin, T. Impacts of anthropogenic and biophysical factors on ecological land using logistic regression and random forest: A case study in Mentougou District, Beijing, China. J. Mt. Sci. 2022; 19, 433\u0026ndash;445. 31https://doi.org/10.1007/s11629-021-7022-x\u003c/li\u003e\n\u003cli\u003eTolessa, T., Senbeta, F., \u0026amp; Kidane, M. The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia. \u003cem\u003eEcosystem Services\u003c/em\u003e. 2017; 47\u0026ndash;54. https://doi.org/10.1016/j.ecoser.2016.11.010\u003c/li\u003e\n\u003cli\u003eTurner MG, Gardner RH, O\u0026rsquo;Neill RV. Landscape Ecology in theory and practice. 2001. New York: Springer-Verlag.\u003c/li\u003e\n\u003cli\u003eTurner, R. K., Paavola, J., Cooper, P., Farber, S., Jessamy, V., \u0026amp; Georgiou, S. Valuing nature: Lessons learned and future research directions. \u003cem\u003eEcological Economics\u003c/em\u003e. 2003;\u003cem\u003e46\u003c/em\u003e(3), 493\u0026ndash;510. https://doi.org/10.1016/S0921-8009(03)00189-7\u003c/li\u003e\n\u003cli\u003eValenteet, J.J., Gannon, D.G., Hightower, J. Toward conciliation in the habitat fragmentation and biodiversity debate. Landsc Ecol. 2023; 38, 2717\u0026ndash;2730. https://doi.org/10.1007/s10980-023-01708-9\u003c/li\u003e\n\u003cli\u003eWang, H., Zhang, M., Wang, C., Wang, K., Wang, C., Li, Y., Bai, X., \u0026amp; Zhou, Y. Spatial and Temporal Changes of Landscape Patterns and Their Effects on Ecosystem Services in the Huaihe River Basin, China. \u003cem\u003eLand\u003c/em\u003e. 2022; 11(4), 1\u0026ndash;19. https://doi.org/10.3390/land11040513\u003c/li\u003e\n\u003cli\u003eWang, Yahui \u0026amp; Dai, Erfu \u0026amp; Le, Yin \u0026amp; Ma, Liang. Land use/land cover change and the effects on ecosystem services in the Hengduan Mountain region, China. Ecosystem Services. 2018; 34. 55-67. 10.1016/j.ecoser.2018.09.008.\u003c/li\u003e\n\u003cli\u003eWatson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo, D.J.,Dokken, D.J. (Eds.). Land Use, Land use Change and Forestry. A Special Report of the Intergovernmental Panel on Climate Change (IPCC). 2000. Cambridge University Press, Cambridge.\u003c/li\u003e\n\u003cli\u003eXu Y, Bi R, Li Y. Effects of anthropogenic and natural environmental factors on the spatial distribution of trace elements in agricultural soils. Ecotoxicol Environ. 2022; Saf 249:114436. 2022; doi: 10.1016/j.ecoenv. 114436. Epub. PMID: 36525951.\u003c/li\u003e\n\u003cli\u003eYang, L., Shen, F., Zhang, L., Cai, Y., Yi, F., \u0026amp; Zhou, C. Quantifying influences of natural and anthropogenic factors on vegetation changes using structural equation modeling: a case study in Jiangsu Province, China. Journal of Cleaner Production. 2020; 124330. doi:10.1016/j.jclepro.2020.124330\u003c/li\u003e\n\u003cli\u003eZambrano, L., Aronson, M. F. J., \u0026amp; Fernandez, T. The Consequences of Landscape Fragmentation on Socio-Ecological Patterns in a Rapidly Developing Urban Area: A Case Study of the National Autonomous University of Mexico. Frontiers in Environmental Science.2019,; 7. doi:10.3389/fenvs.2019.00152. \u003c/li\u003e\n\u003cli\u003eZhao, Q., Wen, Z., Chen, S., Ding, S., \u0026amp; Zhang, M. Quantifying Land Use/Land Cover and Landscape Pattern Changes and Impacts on Ecosystem Services. International Journal of Environmental Research and Public Health. 2019; 17(1), 126. doi:10.3390/ijerph17010126. \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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Landscape fragmentation, Ecosystem Functions, Benefit Transfer Approach, Boma-Gambella Landscape","lastPublishedDoi":"10.21203/rs.3.rs-4259934/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4259934/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLandscape fragmentation plays a crucial role in determining ecosystem service value functions of landscapes. Understanding the relationship between landscape fragmentation and ecosystem services in areas subjected to environmental vulnerability and biodiversity degradation due to anthropogenic and biophysical drivers is a key for improving ecosystem service functions and their sustainability. The study aims to investigate the Spatio-Temporal dynamics of ecosystem service values functions in response to landscape fragmentation in Boma-Gambella Trans-boundary Landscape, Southwest Ethiopia and East South Sudan. The study applied ArcGIS 10.7, FRGSTAT 4.2 and Benefit Transfer Approach to understand the effect of landscape fragmentation on spatial and temporal changes of ecosystem service value functions. The findings indicate that ecosystem service value functions are negatively associated with the increasing fragmentation of the landscapes into core, perforated, edge, and patch areas. The ArcGIS 10.7 results of the transition matrix confirm that a total of 20321.9\u0026nbsp;million ha of forest land has been converted to other land use land cover types. The results of FRAGSTAT 4.2 reveal that the core areas of the landscape in particular has been changed from 1.95\u0026nbsp;million ha in 2009 to 0.88\u0026nbsp;million ha in 2020. These changes and fragmentation result spatial and temporal changes in ecosystem service value functions of the landscape. The results reveal that there were a total of 627.65\u0026nbsp;million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e ecosystem service value function change between 2009 \u0026amp; 2020. The results of the study also reveal that regulating, provisioning, supporting and cultural service value functions of the forest land decreased at a net change of 198.05\u0026nbsp;Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with annual rate of decreasing change (18.0\u0026nbsp;Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) from 2009 t0 2020. Moreover, regulating service value functions of the forest land significantly decreased with a net change of 127.65\u0026nbsp;Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at annual decreasing rate of change (11.6\u0026nbsp;Million \u003cspan\u003e$\u003c/span\u003eUS ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) from 2009 to 2020, indicating a higher susceptibility of the forest land to external factors which have been induced by agricultural land and settlement area expansion. The study, therefore, highlights need for understanding landscape fragmentation impact on ecosystem service value functions and the need to promote conservation and restoration of ecosystem services. The study recommends further investigations using high-resolution satellite imagery; detailed field surveys on the effects of landscape fragmentation on ecosystem service value functions; and facilitate conservation and restoration actions for sustainable utilization of the landscape's biodiversity and ecosystem services at various scales in similar biophysical settings as Boma- Gambella Trans-Boundary Landscape.\u003c/p\u003e","manuscriptTitle":"Spatio-Temporal Dynamics of Ecosystem Service Value Functions in Response to Landscape Fragmentation in Boma-Gambella Trans-Boundary Landscape, Southwest Ethiopia and Eastern South Sudan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 13:51:42","doi":"10.21203/rs.3.rs-4259934/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-05-14T09:01:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-08T18:24:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151534028731874828292997375219715734962","date":"2024-05-08T16:56:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66537248460341553653762013341868343651","date":"2024-04-28T12:07:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95629830755622149969798347652149529429","date":"2024-04-27T15:59:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-24T17:04:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-23T13:13:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-23T13:10:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2024-04-13T00:23:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aaa60fcb-a635-427a-828b-06cf35cc6014","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T06:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 13:51:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4259934","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4259934","identity":"rs-4259934","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}