Analysis Of Relative Importance Research Articles

Analyzing Drivers of Tropical Moist Forest Dynamics in the Kahuzi-Biega National Park Landscape, Eastern Democratic Republic of Congo from 1990 to 2022

The protected areas (PA) of the Democratic Republic of the Congo serve as vital carbon reservoirs and are crucial for biodiversity conservation and climate regulation. Despite their significance, these areas face escalating rates of deforestation and degradation, often poorly understood at the local level. This study focuses on the dynamics of tropical moist forest (TMF) and the relative importance of the driving factors in the landscape of Kahuzi-Biega National Park (KBNP), one of the country’s prominent PAs. Analyzing annual TMF dynamics from 1990 to 2022 using data classified by Vancutsem and his collaborators in 2021 from Landsat imagery alongside spatial datasets of deforestation and degradation drivers, we employed a comprehensive analytical approach. This included meshing, multi-scale analysis, principal component analysis, zoning, multiple linear regression, and relative importance analysis through bootstrapping. The findings indicate that the grid size considered does not significantly influence TMF dynamics in the KBNP landscape (p-value = 0.67, α = 0.05). The edge and outer zones experienced substantial dynamics, with approximately 30% forest cover loss in both areas, contrasting with the relatively stable TMF cover (~100%) in the inner zone. Fire emerged as the most influential driver, explaining TMF dynamics with a relative importance of approximately 55%, 30%, and 23% in the inner, edge, and outer zones, respectively. This study underscores KBNP’s efficacy in curbing TMF loss but highlights the need for enhanced protection around its periphery. Management efforts should prioritize sustainable land use practices, livelihood improvement, and the establishment of an officially recognized buffer zone.

Spatial (Mis)Matches Between Biodiversity and Habitat Quality Under Multi-Scenarios: A Case Study in Shandong Province, Eastern China

Despite declines in biodiversity and habitat quality (HQ) at a global scale, our understanding of the HQ and matches between HQ and biodiversity under management scenarios is incomplete. To address this deficiency, the study examined trends in HQ and (mis)matches between biodiversity and HQ over four decades in Shandong province, China, identified the key drivers, and assessed the effectiveness of ecological policies, including Ecological Redlines (ERLs) and the Grain for Green (GG) program. During the 40-year period, HQ and matching degrees (indicated by related coefficients) between biodiversity and HQ decreased obviously. Correlation analysis showed that related coefficients between HQ and four biodiversity indices (vertebrate, vascular plant, and vegetation formation type richness, and comprehensive biodiversity index) were all significant (p < 0.01), and coefficients were highest for the biodiversity composite index. An analysis of relative importance by the random forest algorithm indicated significant variation in driving factors for spatial distribution of HQ, biodiversity, and matches between them. The key determinants of biodiversity distribution were biophysical factors, such as NDVI (normalized difference vegetation index), DEM (digital elevation model), and temperature. However, the main drivers of HQ distribution were social factors, such as the accessibility of anthropogenic activities, urbanization, and population density. Ecological policy scenarios, ERLs and GG, are clearly effective and could improve HQ and the matching degree between HQ and biodiversity significantly. Furthermore, the improvement in HQ under ERLs was less than that under GG, while the increase in the matching degree was opposite. The results of this study can be integrated by ecological managers and planners for biodiversity conservation.

Comparing the roles of creativity and digital nativity in predicting general and IT innovativeness

PurposeThe study compares the effects of creativity and digital nativity, with the latter signifying proficiency in digital skills, on enhancing innovation among employees. The research specifically evaluates the impact of creativity and digital nativity on general innovative performance and personal innovativeness using IT (PIIT).Design/methodology/approachTwo studies were conducted. Study 1 surveyed 497 individuals on Prolific using objective measures of general innovative performance and self-report measures of PIIT. Study 2 replicated the same research design on Amazon Mechanical Turk with 336 participants and other-rated measures of PIIT.FindingsResults were consistent across both studies and showed that creativity influenced general innovative performance more than digital nativity. However, digital nativity was a stronger predictor of PIIT, above and beyond the nonsignificant effects of creativity.Research limitations/implicationsThis study helps understand the roles that digital nativity and creativity play in general innovative performance and in IT-related innovative performance by providing a relative importance analysis of these components.Practical implicationsWe offer guidance to organizations on how to select individuals and assign them to particular tasks depending on digital or general innovative task requirements.Originality/valueThis is the first study to examine the direct comparison of creativity and digital nativity. Although the literature highlights the importance of digital nativity for innovation, our research reveals that creativity is more important for general innovative performance.

Advancing source apportionment of soil potentially toxic elements using a hybrid model: a case study in urban parks, Beijing, China.

Identifying the source-specific health risks of potentially toxic elements (PTE) in urban park soils is essential for human health protection. However, previous studies have mostly focused on the deterministic source-specific health risks, ignoring the health risk assessment from a probabilistic perspective. To fill this gap, we developed a hybrid model that incorporated machine learning (ML) interpretability into positive matrix factorization (PMF) and probability health risk assessment (PHRA) based on the Monte Carlo simulation. The results indicated that concentrations of soil PTEs except for Mn and Sb were significantly higher than their corresponding background values. Random forest (RF) was regarded as the best ML model to identify key drivers for As, Cd, Cr, Cu, Ni, Pb, and Zn, with R2 > 0.60, but was less effective for other soil PTEs (R2 < 0.49). Specifically, the contributions of the four potential pollutionsources were mixed sources, traffic emission, fuel combustion, and building materials, with contribution rate of 24.88%, 30.56%, 28.99%, and 15.56%, respectively. Fuel combustion contributed the most to non-carcinogenic for children (39.45%), male (43.84%), and female (43.76%), and the non-carcinogenic risk could be considered negligiblefor human. However, building materials was the major contributor to carcinogenic risk for children (36.1%), male (44.9%), and female (43.2%). The integration of the RF model with PMF and PHRA improved the accuracy of the results by identifying and quantifying the specific sources of each soil PTE using the relative importance analysis from the RF model. The results of this study assisted in providing efficient strategies for risk management and control of soil PTEs in Beijing parks.

How to improve public environmental health by facilitating metro usage on weekend: exploring the non-linear and threshold impacts of the built environment.

The accelerated motorization has brought a series of environmental concerns and damaged public environmental health by causing severe air and noise pollution. The advocate of urban rail transit system such as metro is effective to reduce the private car dependence and alleviate associated environmental outcomes. Meanwhile, the increased metro usage can also benefit public and individual health by facilitating physical activities such as walking or cycling to the metro station. Therefore, promoting metro usage by discovering the nonlinear associations between the built environment and metro ridership is critical for the government to benefit public health, while most studies ignored the non-linear and threshold effects of built environment on weekend metro usage. Using multi-source datasets in Shanghai, this study applies Gradient Boosting Decision Trees (GBDT), a nonlinear machine learning approach to estimate the non-linear and threshold effects of the built environment on weekend metro ridership. Results show that land use mixture, distance to CBD, number of bus line, employment density and rooftop density are top five most important variables by both relative importance analysis and Shapley additive explanations (SHAP) values. Employment density and distance to city center are top five important variables by feature importance. According to the Partial Dependence Plots (PDPs), every built environment variable shows non-linear impacts on weekend metro ridership, while most of them have certain effective ranges to facilitate the metro usage. Maximum weekend ridership occurs when land use mixture entropy index is less than 0.7, number of bus lines reaches 35, rooftop density reaches 0.25, and number of bus stops reaches 10. Research findings can not only help government the non-linear and threshold effects of the built environment in planning practice, but also benefit public health by providing practical guidance for policymakers to increase weekend metro usage with station-level built environment optimization.

Hyperparameter selection for dataset-constrained semantic segmentation: Practical machine learning optimization.

This paper provides a pedagogical example for systematic machine learning optimization in small dataset image segmentation, emphasizing hyperparameter selections. A simple process is presented for medical physicists to examine hyperparameter optimization. This is also applied to a case-study, demonstrating the benefit of the method. An unrestricted public Computed Tomography (CT) dataset, with binary organ segmentation, was used to develop a multiclass segmentation model. To start the optimization process, a preliminary manual search of hyperparameters was conducted and from there a grid search identified the most influential result metrics. A total of 658 different models were trained in 2100h, using 13160 effective patients. The quantity of results was analyzed using random forest regression, identifying relative hyperparameter impact. Metric implied segmentation quality (accuracy 96.8%, precision 95.1%) and visual inspection were found to be mismatched. In this work batch normalization was most important, but performance varied with hyperparameters and metrics selected. Targeted grid-search optimization and random forest analysis of relative hyperparameter importance, was an easily implementable sensitivity analysis approach. The proposed optimization method gives a systematic and quantitative approach to something intuitively understood, that hyperparameters change model performance. Even just grid search optimization with random forest analysis presented here can be informative within hardware and data quality/availability limitations, adding confidence to model validity and minimize decision-making risks. By providing a guided methodology, this work helps medical physicists to improve their model optimization, irrespective of specific challenges posed by datasets and model design.

Heterogeneity of factors associated with cognitive decline and cortical atrophy in early- versus late-onset Alzheimer’s disease

The objectives of this study were to investigate the variable factors associated with cognitive function and cortical atrophy and estimated variable importance of those factors in affecting cognitive function and cortical atrophy in patients with EOAD and LOAD. Patients with EOAD (n = 40), LOAD (n = 34), and healthy volunteers with normal cognition were included (n = 65). All of them performed 3T MRI, [18F]THK5351 PET (THK), [18F]flutemetamol PET (FLUTE), and detailed neuropsychological tests. To investigate factors associated with neuropsychological test results and cortical thickness in each group, we conducted multivariable linear regression models, including amyloid, tau, cerebral small vessel disease markers on MRI, and vascular risk factors. Then, we estimated variable importance in associating cognitive functions and cortical thickness, using relative importance analysis. In patients with EOAD, global THK retention was the most important contributor to the model variances for most neuropsychological tests, except for memory. However, in patients with LOAD, multiple contributors beyond tau were important in explaining variance of neuropsychological tests. In analyses with mean cortical thickness, global THK retention was the main contributor in patients with EOAD, while in LOAD patients, multiple factors contributed equally to mean cortical thickness. Therefore, EOAD and LOAD may have different pathomechanistic courses.

A network analysis of daily stressors, subjective well-being, psychological distress and psychological capital during the COVID-19 pandemic

BackgroundThere is already considerable evidence suggesting the potential existence of a comprehensive system that reflects how individuals utilize psychological capital (PsyCap) to preserve mental health amid daily stressors during the COVID-19 pandemic. To explore the underlying mechanism of this system, the current study is the first to use network analysis showing the dimension-level correlation patterns of daily stressors, subjective well-being (SWB), psychological distress and PsyCap during the pandemic. MethodWe recruited 1556 participants in China and assessed daily stressors, SWB, psychological distress, and PsyCap through self-report questionnaires. A dimension-level network analysis was conducted to identify key dimensions and their associations. Relative importance analysis examined the contribution of each PsyCap dimension to SWB and psychological distress. ResultDepression, anxiety and stress demonstrated high strength, and life satisfaction exhibited the highest bridge strength in the network. The great majority dimensions of daily stressors had positive connections with stress and anxiety, while financial restrictions and dissatisfaction with education/occupation had negative connections with life satisfaction. Self-efficacy and optimism were positively connected with life satisfaction. Hope and resilience were negatively connected with stress, as well as hope and optimism were negatively connected with depression. Optimism and self-efficacy made the highest contribution to SWB, while resilience made the highest contribution to psychological distress among PsyCap dimensions. ConclusionThe findings elucidate the intricate relationship between daily stressors, SWB, psychological distress and PsyCap during the pandemic. Targeted interventions focusing on the specific PsyCap dimensions may enhance mental health outcomes in the post-COVID-19 era.

The conjoint effects of corporate social responsibility performance and report tone on financial constraints: Evidence from China

Using corporate social responsibility (CSR) disclosure data from Chinese A-share listed firms between 2008 and 2022, we examine the conjoint impact of CSR performance and the tone of CSR reports on financial constraints. The research reveals a significant negative correlation between CSR performance and financial constraints, indicating that the better a firm's CSR performance, the fewer financial constraints it faces. The tone of CSR reports is also negatively correlated with financial constraints. It has a synergy effect with CSR performance, enhancing the alleviating effect of CSR performance on financial constraints. After conducting a series of robustness tests, such as addressing endogeneity and substituting variables, the main results remain robust. Furthermore, relative importance analysis shows that the impact of CSR performance on financial constraints is significantly greater than the impact of the tone of CSR reports. In additional analysis, we also examine the effects of corporate disclosure of creditor protection information, the presence of deficiencies in information, and firm transparency. Against the backdrop of heightened economic volatility and escalating risks, our findings offer valuable guidance on how firms can strategically utilize social responsibility measures to mitigate financial constraints.

Machine learning predict the degradation efficiency of aqueous refractory organic pollutants by ultrasound-based advanced oxidation processes

Ultrasound based advanced oxidation processes (AOPs) are effective for removing refractory organic pollutants by generating reactive species. Machine learning (ML) can systematically provide an excellent opportunity to determine the relationship between feature variables and output variables through large amounts of data, thereby reducing the need for experimental measurements. In this study, a ML approach was applied for the first time to predict the degradation efficiency of aqueous refractory organic pollutants by ultrasound-based AOPs. The obtained dataset was normalized and introduced into four ML models. Among them, Random Forest Regressor and XGB Regressor models had the best fit with 0.9546 and 0.9749 for training set R2 and 0.9548 and 0.9740 for test set R2, respectively. Relative importance analysis and SHAP analysis showed that catalyst type, oxidant concentration, reaction time, and catalyst dosage are the important variables affecting the degradation efficiency of refractory organic pollutants. In addition, excessive ultrasonic intensity, ultrasonic frequency, oxidant concentration, and catalyst dosage had little effect on the increase of degradation efficiency. Based on the descriptive data analysis, high degradation efficiency was obtained with ultrasonic intensity of 2 W·mL−1, ultrasonic frequency of 20–40 kHz, reaction time of 60–90 min, pH 3–7, oxidant concentration of ∼7.5 mmol·L−1, and catalyst dosage of >0.4 g·L−1. This work demonstrated a new predictive method to understand the comprehensive influence of operative factors on degradation efficiency in US-based AOPs, guiding parameter and process optimization.

Exploring drivers of success in partnerships that support federal trail management in the United States

The use of partnerships with external stakeholders to support federal land management objectives is a common practice amongst government agencies. At the US Forest Service and National Park Service, partnerships provide critical support to each agency’s recreation stewardship objectives. While partnerships help these agencies increase capacity and meet recreation objectives, literature on federal land management partnerships typically focuses on large-scale collaborations and single case studies. Little remains known about how inputs and processes relate to partnership success across multiple individual partnerships to support federal trail management objectives. This exploratory study examines the perceptions of agency and partner managers in a sample of 89 partnerships to support trail work at the US Forest Service and National Park Service. Using linear regression and relative importance analysis, results show that trust, capacity, and interdependence are the most salient factors to partnership success. Furthermore, our analysis reveals a secondary set of factors that should also be considered when attempting to improve partnership outcomes.

Relative contribution of comorbid diseases to health-related quality of life in patients with Parkinson’s disease

BackgroundMultimorbidity is common in elderly people, and one of the major consequences of multimorbidity is low health-related quality of life (HRQoL). The aim of this study was to investigate the frequency of comorbid diseases in patients with Parkinson’s disease (PD) and to analyze their relative importance in HRQoL. The aim was also to examine agreement between the generic 15D questionnaire and the PD-specific Parkinson’s Disease Questionnaire (PDQ-8) to further validate 15D in the evaluation of HRQoL in patients with PD.MethodsPatients with PD (N = 551) filled a questionnaire on comorbid diseases, and the 15D questionnaire yielding a 15-dimensional health profile and a score representing the overall HRQoL. Self-organizing map was used for an unsupervised pattern recognition of the health profiles. Relative importance analysis was used to evaluate the contribution of 16 comorbid diseases to the 15D score. The agreement between 15D and PDQ-8 questionnaires was studied in a subset of 81 patients that were examined clinically.Results533 patients (96.7%) reported comorbid diseases. The most affected dimensions in the 15D questionnaire were secretion, usual activities, discomfort and symptoms, and sexual activity. Self-organizing map identified three patterns of health profiles that included patients with high, low or transition HRQoL. The transition subgroup was similar to low HRQoL subgroup in non-motor dimensions. Sixteen comorbid diseases explained 33.7% of the variance in the 15D score. Memory deficit, depression, heart failure, and atrial fibrillation had the highest relative importance. The intraclass correlation coefficient between the generic 15D and the PD-specific PDQ-8 was 0.642 suggesting moderate reliability.ConclusionsThe most marked differences in HRQoL were in the dimensions of secretion, usual activities, and sexual activity. Pattern detection of 15D health dimensions enabled the detection of a subgroup with disproportionately poor HRQoL in non-motor dimensions. The comorbid diseases affecting most to HRQoL were memory deficit and depression. The generic 15D questionnaire can be used in the evaluation of HRQoL in PD patients.

건설현장의 스마트 안전보건활동 요소에 관한 중요도 분석

The purpose of this study is to analyze the importance of smart safety and health activity elements in order to increase the execution power and effectiveness of smart safety and health activities at construction sites. To this end, in this study, the elements of smart safety and health activities were derived from the technical, educational, and management levels, and AHP(Analytic Hierarchy Process) survey data were collected and analyzed for safety-related experts at construction sites. As a result of the relative importance analysis of the elements of smart safety and health activities, the priority of smart experience education programs was the highest, and smart risk assessment system, smart safety control system operation, smart worker safety management, smart work site management, smart technology specialized training instructors, smart risk area access management, safety and health management system, smart education certification system, smart equipment management, and smart non-face-to-face education were sequentially high. The results of this analysis can be evaluated as the most effective way to prevent and act on safety accidents in construction sites and situations using Augmented Reality(AR) and VR(Virtual Reality), and it needs to be introduced first. In addition, in order to prevent and reduce various industrial accidents at construction sites, it suggests that smart safety and health activities at the enterprise level, such as smart experience education, risk assessment, and integrated safety management systems, are needed rather than smart safety and health activities at the individual level.

Leveraging Remote Sensing-Derived Dynamic Crop Growth Information for Improved Soil Property Prediction in Farmlands

Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology’s robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14–45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping.

H2S mitigation for biogas upgrading in a full-scale anaerobic digestion process by using artificial neural network modeling

Biogas production by anaerobic digestion (AD) has emerged as a prominent bio-renewable energy source in recent years. However, the process also produces undesirable by-products, including H2S, thereby negatively impacting the biogas quality. This study focused on mitigating H2S in a full-scale AD located at a wastewater treatment plant (WWTP) by controlling the internal operational parameters by using an artificial neural network (ANN) model. Data from 54 days of AD operation were used to train and validate a structured ANN with a 5-3-1 topology. To minimize the H2S content, optimum values for dry solid (DS), volatile solid (VS), pH, temperature, and primary sludge fraction (PS) were determined to be 6.2 %, 63 %, 7.7, 35.6 °C, and 67.6 %, respectively, using the particle swarm optimization (PSO) algorithm. This optimization indicated a 49 % reduction in the average H2S concentration, from 6117 ppm to 3107 ppm. The analysis of relative importance (RI) showed that the pH (RI = −29.5) and PS (RI = −28.7) were the most critical factors affecting biogas quality. Additionally, several solutions derived from the optimization results were practically implemented in the Qom WWTP to achieve optimal conditions, and the outcomes were discussed.

Relative importance analysis for count regression models

Relative importance analysis for count regression models

Synergizing remote sensing and ecological indicators (RSEIs) for evaluating ecological environmental quality (EEQ) in Asansol Municipal Corporation: an integrated approach.

Human activities have dramatically affected global ecology over the past few decades. Geospatial technologies provide quick, efficient, and quantitative evaluation of spatiotemporal changes in eco-environmental quality (EEQ). This study focuses on a novel approach called remote sensing-based ecological indicators (RSEIs), which has used Landsat imagery data to assess environmental conditions and their changing trends. Four ecological indicators, mainly heatness, dryness, wetness, and greenness, have been used to assess the EEQ in Asansol Municipal Corporation Region (AMCR). Assembling all the indicators to generate RSEI, the principal component analysis (PCA) approach was applied. Our findings show that wetness and greenness favorably impact the province's EEQ, whereas dryness and heat create a negative impact. The RSEI assessment revealed that 24.53 to 28.83% of the area was poor and very poor, whereas the areas with very good decreased from 18.80 to 4.01% from 2001 to 2021 due to urban expansion and industrialization. The relative importance analysis indicates that greenness has a positive relation with RSEI, and dryness and heatness have a negative relation with RSEI. Finally, the receiving operating characteristic (ROC) was used for validation (AUC-0.885) of the RSEI. This study offers valuable insights for ecological management decision-making, guiding planners, and policymakers.

The necessity of job design for employee creativity and innovation: nothing happens without supervisor support

ABSTRACT Job design is a favourable antecedent of discretionary innovative work behaviours (IWB). Although multiple job characteristics (i.e., job demands and job resources) define the work environment, only a few are supposed to be of dominant and critical importance for employee creativity and innovation. By complementing the common sufficient causality with necessity theorizing, we explored the creativity-enabling and innovation-enabling nature of the Job Demands – Control – Support (JDCS) model components. A multi-method approach (i.e., multiple regression, relative importance analysis, and necessary condition analysis) was applied in a comparative contextualized research design (i.e., a three-study field survey research) involving 358 employees and 86 supervisors from an EU member country. The converging results showed that without experiencing vitally important support from a supervisor, enhancing other job characteristics will not lead to high levels of subordinate’s IWB. The present research also recognized work autonomy as an additional task-related critical success factor, which was only necessary when considering employee innovation as a frequently occurring time-lagged outcome. Contrary to our theory-derived assumptions, job complexity as a representative type of job demand was not necessary for IWB. Practitioners may benefit from the findings because they can better understand the prerequisites and priorities for enabling employees’ IWB via job design.

Utility of Single Items within the Suicidal Behaviors Questionnaire-Revised (SBQ-R): A Bayesian Network Approach and Relative Importance Analysis.

The Suicidal Behaviors Questionnaire-Revised (SBQ-R) comprises four content-specific items widely used to assess the history of suicide-related thoughts, plans or attempts, frequency of suicidal ideation, communication of intent to die by suicide and self-reported likelihood of a suicide attempt. Each item focuses on a specific parameter of the suicide-related thoughts and behaviors construct. Past research has primarily focused on the total score. This study used Bayesian network modeling and relative importance analyses on SBQ-R data from 1160 U.S. and 1141 Chinese undergraduate students. The Bayesian network analysis results showed that Item 1 is suitable for identifying other parameters of the suicide-related thoughts and behaviors construct. The results of the relative importance analysis further highlighted the relevancy of each SBQ-R item score when examining evidence for suicide-related thoughts and behaviors. These findings provided empirical support for using the SBQ-R item scores to understand the performances of different suicide-related behavior parameters. Further, they demonstrated the potential value of examining individual item-level responses to offer clinically meaningful insights. To conclude, the SBQ-R allows for the evaluation of each critical suicide-related thought and behavior parameter and the overall suicide risk.

Ecosystem-based spatial modeling: Assessing the supply of hydrological services in a watershed in Southern Brazil

Hydrologic services describe the way terrestrial ecosystems affect freshwater resources. This study employed an ecosystem-based approach and spatial models to assess the significance of ecosystems in providing hydrological services in a Watershed in southern Brazil. The environmental systems and hydrological services were characterized and evaluated for the significance weights assignment by expert opinion dynamics. Spatial and tabular data generated were applied in an ecosystem services model (InVEST-Integrated Valuation of Ecosystem Services and Trade-offs) for a relative importance analysis. Map algebra was performed to integrate relative importance with runoff values, resulting in an importance index (II), which enabled the identification of the most important ecosystems for the hydrological services provision. Among the environmental systems, riparian forests (0.2457), forests (0.2455), wetlands (0.2332), and fields with livestock (0.2291) demonstrated the highest II values. Ranking environmental systems and their vegetation cover based on their performance in terms of eco-hydrological functions and hydrological services has the potential to support management actions on land use in the pursue of sustainability regarding water resources use.