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Article

Proposal of a Socio-Ecological Resilience Integrated Index (SERII) for Colombia, South America (1985–2022)

by
Cesar Augusto Ruiz-Agudelo
1,2
1
RINA-Consulting, 16129 Genoa, Italy
2
Facultad de Ciencias Naturales e Ingeniería, Universidad de Bogotá Jorge Tadeo Lozano, Bogotá 110311, Colombia
Sustainability 2025, 17(14), 6461; https://doi.org/10.3390/su17146461
Submission received: 29 April 2025 / Revised: 2 July 2025 / Accepted: 12 July 2025 / Published: 15 July 2025
(This article belongs to the Special Issue Ecosystem Services and Sustainable Development of Human Health)
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Abstract

Colombia is a megadiverse, multiethnic, and multicultural country with a tremendous socio-ecological systems (SESs) diversity, which faces essential challenges arising from human activities, low levels of sustainable economic development, poverty, and social inequality rates, and the persistence of multiple forms of military, political, and social violence. Understanding the resilience of this complex system is both fundamental and challenging due to the contradictory effects of economic development and regional ecosystem degradation. This research proposes the Socio-Ecological Resilience Integrated Index (SERII) to assess historical changes in socio-ecological resilience in Colombia’s departments (political-administrative units) between 1985–2022. The SERII considers the trade-offs between ecosystems, social systems, and production systems, providing a complete perspective of integrated management with a geographic resolution at the level of general political-administrative units. The results reveal a spatial variation in the SERII, with worse conditions in the Caribbean, the Pacific, and the Colombian Amazon (on the country periphery) and better conditions in departments of the country center. From 1985 to 2022, the SERII experienced a decrease (51.5%), driven by ecosystem degradation, increased extractive activities (illegal and illegal), and the persistence of military, political, and social violence. While the limitations of the proposed indicator are described, the SERII effectively replicates the overall resilience of Colombia’s departments to external shocks and allows for suggesting regional management priorities for the targeted promotion of sustainable development.

1. Introduction

Resilience is the capability of a system to persist after a shock and to reorder itself while preserving the same essential functions [1,2,3]. The concept of resilience was initially used by [1] to define ecosystem resilience, but at this time it is applied in multiple contexts and research questions. In the growing literature on resilience, there are two contrasting concepts. The first is described by [4] as engineering resilience and by [5] as the capability to persist in a disaster with the most minor damage. According to [6], engineering resilience is when a system recovers to its previous state after a disturbance, considering the time needed and the recovery pattern.
Another resilience concept, ecological resilience, is the quantity of perturbation a system can withstand without changing its structure and function [1,2,7]. Ecological resilience contains the concept of regime shifts, i.e., non-linear and abrupt transitions of a system between alternative states that differ in their configuration and properties [8,9,10]. Indeed, a highly resilient system can withstand more stresses, avoiding tipping points and associated regime shifts [8,11]. Resilience is the system’s capacity to adjust to perturbations, granting sustainable development [3].
Perceptions of resilience have evolved rapidly in recent years and now influence an ever-wider range of academic fields [12,13,14,15,16,17,18]. Likewise, multiple methods for measuring resilience have increased, including index- and model-based assessments of ecological resilience [19,20,21], spatial modeling of ecological resilience to climate change [22,23,24,25], assessment of ecological resilience to changes in the supply and demand of ecosystem services [26,27,28,29], assessments of regional resilience and its productive systems [30,31,32,33,34,35], models and indices for assessing urban resilience [36,37,38,39], indices and models of resilience in socio-ecological systems [40,41,42,43,44,45], and approaches to the economic understanding of resilience [46,47,48]. Marginally, some contributions are reported in the literature involving the effect of violence (military, political, and social) on socio-ecological resilience [49,50,51,52,53,54,55,56], as well as the effect of equity and social justice on social resilience [57,58,59,60]. The wide variety of resilience assessment concepts and methods makes it difficult to detect common characteristics. However, almost all descriptions emphasize the ability to adapt effectively to shocks.
In response to this conceptual and methodological multiplicity, socio-ecological resilience (SER) assessment has appeared. This is an addition to conventional resilience theory, which focuses on evaluating the capacity of socio-ecological systems (SESs) to withstand external shocks by exploring their interdependencies [45,61]. While SER was initially approached qualitatively and descriptively [62], modern research has developed quantitative methodologies involving expert assessments, participatory surveys, and fuzzy assessments, supported by indicators spanning ecological processes as well as economic and socio-cultural activities to quantify SER within SESs [45,63,64]. This approach recognizes that the complex spatial diversity inherent to multiple SESs (derived from land uses, landscape compositions, economic and cultural dynamics, and ecosystem structures) makes assessing SER across multiple systems difficult. Therefore, it is recommended that multidisciplinary indices be used to balance stakeholders’ interests. For example, [45] implemented a methodological approach called CLSER (socio-ecological resilience of coastal systems) that focused on coastal areas, incorporating multiple indices, to assess comprehensive SER.
This research proposes a novel approach for evaluating SER in a broad context. For this purpose, the Colombian territory was selected as a case study, a megadiverse country that has witnessed multiple socio-environmental shocks, such as the expansion of illegal mining [65], the expansion of illicit crops and deforestation [66,67,68,69], the increase in monocultures [70,71], and the historical persistence of political, social, and military violence [72,73,74]. In a context like Colombia, addressing the limitations of evaluating SER is imperative. This study considers the processes of three subsystems with conflicts of interest (ecological, social, and productive) to assess the spatiotemporal variation in SER preliminarily and comprehensively at the level of political-administrative divisions in Colombia for the period 1985–2022 and with limited statistical information.
The main objectives of this research are the following:
  • The initial identification of the resilience dynamics of the subsystems for the Colombian territory between 1985–2022;
  • The evaluation of the historical trajectories of SER changes throughout a complex and widely diverse territory;
  • The identification of the driving forces that explain the transformation of SER between 1985 and 2022.
Additionally, this research contributes to the literature on SER evaluated in two ways: (1) It presents a new case study of SER evaluation for a developing country with a history marked by violence (political, military, and social) and megadiverse, which is novel in the literature, given the multiple diversity of methods and applications; (2) it presents an expanded methodological approach for evaluating SER with multiple indices, exploring the interactions between the ecological, social, and productive subsystems.

2. Methods and Materials

2.1. Study Area

The Colombian territory has a land surface of 1,141,748 km2, which ranges from the great Amazon region to Panama boundaries, being crossed by the Andes mountain range that in Colombia is divided into three branches (Eastern, Central, and Western). Additionally, Colombia has coasts on the Pacific and Atlantic oceans, with a maritime extension of 928,660 km2 (Figure 1). According to [75], the Colombian population is 48,258,494 inhabitants. The Colombian territory is structured into 32 administrative areas (departments) and a capital district (Bogotá).
Colombia is one of the most populous countries on the American continent. The majority of the population resides in the Andean and Caribbean regions. In addition, it is a country recognized for its biological megadiversity [76]. Additionally, Colombia is the protagonist of Latin America’s oldest armed conflict [77]. The violence between political parties (liberals and conservatives, the 1950s) gave birth to different guerrilla groups. They are fostering the subsequent emergence of radical right-wing groups such as the United Self-Defense Groups of Colombia (AUC). Now, residual groups such as Criminal Bands (BACRIM), drug trafficking groups (Clan del Golfo, for example), and guerrillas remain, reinforcing this dynamic of permanent conflict in recent Colombian history.
The permanent armed conflict between multiple illegal groups and the Colombian government has been a central historical determining factor in the country’s land cover dynamic [69,78,79]. Agreeing with Gonzalez-Gonzalez et al. [80], violence is related to deforestation in the country. However, the conflict’s influence on every Colombian geographical region is specific and acts with other drivers, generating different trends in ecosystem alteration.

2.2. The Socio-Ecological Resilience Integrated Index (SERII)

Based on Wu et al.’s [45] proposal of interdependence between social systems and ecosystems, this research proposes an index that incorporates ecosystem resilience (ER), social systems’ resilience (SR), and production systems’ resilience (PR). This aims to evaluate the socio-ecological resilience of the Colombian departments from 1985 to 2022. The detailed parameters used for each subsystem can be found in Table 1 (1985) and Table 2 (2022).
As observed in Table 1 and Table 2, the parameters for the comparison of the subsystems and principles of the SERII (1985–2022) are different; this is due to the limitations of existing information in the country’s statistical systems (a situation that is common in many developing countries). However, for the present approach, the interdependencies of the ER subsystems (principles: ecosystem function, landscape diversity, ecological protection, ecological stress), SR (principles: development level, economic support, cultural maintenance of natural conditions, demographics, regional military violence, events of armed violence against civilians, poverty), and PR (principles: production ability, diversity of agricultural and fishery products, natural condition, production ability loss) are compared. As mentioned above, and by the theoretical guidelines of Wu et al. [45], one of the objectives of this research is to operationalize a SERII; therefore, the comparisons must be understood at the subsystem and principles level. It is recommended that future studies, with a more complete public information base, standardize the parameters used in this initial approach.

2.2.1. Ecosystem Resilience Assessment (ER)

This study assessed ER by examining parameters that represent the following principles: (1) ecosystem function [34,35,36] through the calculation of the remnant natural capital (RNC) after human intervention in each Colombian department (1985 and 2022), under the consideration that more significant natural capital is expected to result in greater ecological resilience; (2) landscape diversity [20,44,45,85,86], considering that a greater diversity of biomes (for 1985 and 2022) is an adequate indicator of the landscape diversity for each department; (3) ecological protection [29,39,88,89,90], through the estimation of the coverage of protected areas by the department for 1985 and 2022, considering that an increase in the extension of legally protected areas is desirable for ecological resilience; (4) biodiversity loss [19,21,25,42,46,92,125,126], through the calculation of the loss of natural cover for 1985 and 2022 [93], and the number of threatened species by department, only for 2022 (this due to the limitations of statistical information on biodiversity in Colombia); (5) ecological stress [23,45] by estimating the natural capital loss (1985 and 2022). Finally, the principle of biological diversity was evaluated only for 2022 [22,24,120,121,122,123] by calculating the total reported species number (2022) per Colombian departments (https://mol.org/regions/ Accessed on 3 October 2024). Table 1 and Table 2 describe these principles and parameters and the sign (+ or −) considered for calculating the ER (Table 1 and Table 2).

2.2.2. Social System Resilience (SR)

According to Sapirstein [98] and Wu et al. [45], social resilience (SR) is an essential factor in maintaining the structure and function of human communities in the face of external pressures arising from social and political changes. To quantify the political and social impact of these changes in the Colombian departments, this study focuses on eight principles: development level, economic support, cultural maintenance of natural conditions, demographic pressure, military violence, events of armed violence against civilians, poverty level, and permanent unemployment (Table 1 and Table 2).
For the assessment of the development level [40,41,47,95,96], the national statistics of total GDP by departments for 1985 and 2022 were considered [97]. Economic growth, as evidenced by the regional economic structure and development level, is indispensable in assessing a region’s ability to manage external pressures [41].
Economic support refers to the strength of economic or productive activities, which are fundamental in maintaining regional resilience [45]. Primary industry is a critical contributor to species diversity conservation [30,31,98,99,100]. For this study, the use of national statistics of primary industry GDP (1985 and 2022) per Colombian departments [97] is proposed to imitate economic support, where higher GDP values indicate higher resilience of farmers’ livelihoods [45,130]. Additionally, only for 2022 (due to limitations of existing information), poverty alleviation statistics [97] were used as another proxy for the improvement of economic support for Colombia [98].
The cultural maintenance of natural conditions principle was considered using national literacy rate statistics for 1985 and 2022 [97]. The educational level reflects the local cultural maintenance [42,43,44,101,102]. On the other hand, the poverty level is a condition that potentially generates a decrease in social resilience and potential pressure on ecological resilience [57,58,59,60,110,111]. According to the levels of information available, this research used the available statistics on poverty (unmet basic needs) for 1985 [97] and multidimensional poverty for 2022 [97]. In addition to these indicators, national unemployment statistics were used only for 2022, considering that an increase in unemployment decreases the SR [37,106,127].
It is internationally recognized that demographic shift is another critical principle of social resilience, and population pressure is an acceptable indicator of the pressure on the socioeconomic system [20,103,104,105,106,107]. Wu et al. [45] propose the population growth rate to explain population pressure.
R j = ( P j − P i ) / ( j − i )
where R j is the population rate change (by department), and P i and P j are the population in the i-th and the j-th year.
Finally, and particularly in Colombia, it is crucial to consider the effect of military, political, and social violence on social resilience. Some studies have begun to recognize, at least preliminarily, that these phenomena of violence, when persistent and massive, can decrease the resilience of a socio-ecological system [50,51,53,55,56,109]. The statistics available at the national level of war actions per Colombian department for 1985 and 2022 and population attacks for 1985 and 2022 were used [108].

2.2.3. Production System Resilience (PR)

According to Wu et al. [45], food provision is necessary for any human society. PR plays a vital role in the efficient use of agricultural resources and the sustainable development of species diversity in Colombian ecosystems [61]. This study proposes to measure PR considering the following five principles: production ability, natural conditions, social support, natural condition loss, and production ability loss (Table 1 and Table 2).
Given the disparate levels of information available in Colombia, production ability understood as the diversity of agricultural and livestock production [33,45,112] was assessed through the agricultural GDP per Colombian departments’ [97] statistics for 1985. For 2022, access was gained to the statistics on the number of production units per department in the 2022 National Agricultural Survey [97]. The diversity and number of agricultural productive units in each Colombian department are reflected in these statistics, which only became available in 2018. For 2022, the 2022 National Agricultural Survey [97] identified the number of agricultural units with access to communications in addition to the above. This last parameter considers the assumption that the greater the access of agricultural production units to communication technologies (telephone, internet, social media, among others), the greater their production ability.
The natural condition, understood as the environmental conditions guaranteeing the diversity of agricultural spaces facilitating their development [42,114], was evaluated in 1985 through biome diversity [87]. In addition to the above, and only for 2022, the principle of social support was considered through the use of the information available from the population working in agricultural systems in 2022 [97] and the professional workforce of farming units in 2022 [97]. Labor input and skilled labor input represent the allocation of human capital for the primary industry in the Colombian departments [32,33,45,112].
From the theoretical approach of this study, it is essential to evaluate the principle of natural condition loss, understood as the pressure factors that can affect the socio-ecological bases of agricultural production [48,115,116,117,128,129]. For these purposes, information on extractive activities GDP in 1985 and 2022, per Colombian departments [97], was used, assuming that the increase in extractive activities (mining and oil/gas) alters the natural basis of agricultural production. In addition to the above, and only for 2022, information on access to poor-quality water in 2022 [97] was used, considering that the deterioration of water quality also affects agricultural production conditions.
Finally, the principle of production ability loss was evaluated, considering that the parameters forced displacement for 1985 and 2022 and violent land dispossession for 1985 [108] also explain the deterioration of the rural agricultural production base, given the consequences of the armed conflict (rural displacement, loss and accumulation of productive land) in the Colombian departments’ productive structure [49,52,53,54,56,60,119].

2.2.4. Normalization, Spatialization, and Weight Calculation of Parameters

Normalization processing was operated on each indicator’s raw data to address unrelated units in the data. Given the differential impact of positive and negative resilience indicators, the fuzzy membership function method [45,131] was applied for dimensionless processing. The normalization formula applied is as follows [45]:
Positive   indicator r : X i j = x i j − ʎ j m i n ʎ j m a x − ʎ j m i n   ( i = 1 ,   2 … n ; j = 1 ,   2 … m )
Negative   indicator : X i j = ʎ j m i n − x i j ʎ j m a x − ʎ j m i n   ( i = 1 ,   2 … n ; j = 1 ,   2 … m )
where X i j is the original data and ʎ j m a x and ʎ j m i n are the maximum and minimum records of the original data, respectively.
The study’s scale was standardized by the official database of Colombia’s political divisions [87]. A spatial interpolation was performed at the level of Colombian departments to map indicators without spatial attributes, such as socioeconomic data. All spatial analyses were completed under the R program’s spatial analysis function, and all the maps were edited in QGIS (version 3.16.15).
Following the work of Wu et al. [45], the objective entropy weighting method was used to establish the parameter weights. Entropy has been broadly used in various disciplines, including data science, natural science, and social science [132,133], to indicate the direction of system change. The entropy weighting method gives weights to indicators based on their change degree. Utilizing the entropy method to determine the indicator weights eludes the problems of randomness and determinism of subjective weighting methods. According to [133], indicators with greater entropy, smaller degrees of variation, and less information receive smaller weights, while indicators with smaller entropy receive larger weights. Using the entropy method to determine indicator weights effectively resolves the problem of information overlapping among multiple indicator variables.
If the indicator data matrix X = X i j n ∗ m consists of n evaluation units and m evaluation indicators, the specific calculation process is as follows [38]:
Step I: Establish the proportion of indicators ( S i j ):
S i j = X i j / ∑ i = 1 n X i j ( J = 1,2 , … m )
Step II: Calculate the entropy of the indicators ( e j ):
e j = − 1 l n m x   ∑ i = 1 n S i j   l n   S i j   ( 0   ≤   e j   ≤ 1 )
Step III: Establish the difference coefficient ( g j ) :
g j = 1 − e j
Step IV: Define the weight of the indicators ( w j ):
w j = g j / ∑ j = 1 m g j

2.3. Calculation of the Socio-Ecological Resilience Integrated Index (SERII)

The resilience of each subsystem was estimated based on the weight indicators by the following equations [38]:
E R i = ∑ j = 1 n W j I i j
S R i = ∑ j = 1 n W j I i j
P R i = ∑ j = 1 n W j I i j
where W j is the weight of the j-th indicator of each system and I i j is the j-th indicator of the i-th assessment unit. Finally, the SERII was calculated using the following equation:
S E R I I i = α 1 E R i + α 2 S R i + α 3 P R i
where E R i , S R i , P R i , and SERII are the ER, SR, PR, and SERII of the i-th assessment unit, respectively. α 1 , α 2 , and α 3 are the weights of the ER, SR, and PR, respectively, which are determined by the concept of entropy using Equations (4)–(7).

2.4. Spatiotemporal Change in SERII

For the spatiotemporal change in the SERII, this research utilized the management score (MS) [38,45] to show the variation in the SERII across Colombian departments from 1985–2022.
M a n a g e m e n t   S c o r e = S E R I I t a r g e t   2022 − S E R I I b a s e   1985 / S E R I I b a s e   1985
where S E R I I t a r g e t   2022 and S E R I I b a s e   1985 are the SERII of the target year and the base year, respectively. The natural breaks method was used to classify all indices, including the ER, SR, PR, and SERII, into five classes (Table 3) for a quick evaluation on the Q-GIS (version 3.16.15).

3. Results

3.1. Weight of Parameters of Subsystems

The results show that, on the one hand, in 1985, SR and PR played the most critical role in evaluating the SERII for positive indicators. For negative indicators, ER has the highest weight. For each subsystem, the remnant natural capital (0.50—ER), the natural capital loss (0.83—ER), the total GDP (0.61—SR), the population attacks (0.41—SR), the agricultural GDP (0.67—PR), and violent land dispossession (0.35—PR) are the key parameters (Figure 2).
On the other hand, for 2022, the ER has the highest weights in the SERII evaluation for both positive and negative indicators. For each subsystem, the remnant natural capital (0.62—ER), the loss of natural capital (0.79—ER), the primary production GDP (0.49—SR), the population attacks (0.33—SR), the number of productive units (0.35—PR), and the extractive activities GDP (0.63—PR) are the key parameters (Figure 3).
Violence parameters, such as attacks on the population, significantly affect the calculation of the SERII for 1985 and 2022.

3.2. Spatiotemporal Changes in ER, SR, and PR 1985–2022

3.2.1. Spatiotemporal Changes in ER 1985–2022

The distribution of ER in the Colombian departments showed a significant spatial variation. For 1985 and 2022, the lowest ER values were concentrated in the departments of the Caribbean region (Figure 4 and Table S1). The department’s proportion with the lowest ER values in 1985 (very low and low) was 36%; by 2022, this proportion dropped to 21%. The management score (MS) for the departments of Colombia was used to examine historical changes in ER from 1985 to 2022, where the departments of Magdalena, Bolivar, Sucre, Cordoba, Antioquia, Casanare, Meta, Vichada, Putumayo, Tolima, San Andres and Providencia, and Caldas maintain a trend towards a very low ER for this period. For the rest of the country, the ER trend is towards a slight improvement (in the middle to high classes) by 2022, possibly explained by the increase in and consolidation of the national protected area system and a lower relative loss of remnant natural capital.

3.2.2. Spatiotemporal Changes in SR 1985–2022

The spatial distribution of SR is more constant and localized for the country context between 1985–2022 (Figure 5, Table S2). The proportion of departments with lower SR values in 1985 (very low and low) was 63.6%; by 2022, this proportion dropped to 39.4%. The management score (MS) for the departments of Colombia was used to examine historical changes in SR from 1985 to 2022, where the departments of La Guajira Córdoba, Sucre, Choco, Caldas, Arauca, Putumayo, and Vaupes maintain a trend towards a very low SR for 1985–2022. In general terms, for the rest of the country, the SR trend is towards a slight improvement (in the middle to high classes), perhaps explained by the decrease in and relocalization of the incidence of armed, political, and social violence phenomena and a slight increase in the GDP of agricultural production in this period.

3.2.3. Spatiotemporal Changes in PR 1985–2022

The spatial distribution of PR is very diverse, with a marked downward trend between 1985 and 2022 (Figure 6, Table S3). The department’s proportion with lower PR values in 1985 (very low and low) was 24%; by 2022, this proportion had increased to 48%. The management score (MS) for the departments of Colombia was used to examine historical changes in PR from 1985 to 2022, where the departments of La Guajira, Cesar, Bolivar, Sucre, Choco, Putumayo, Caquetá, Vaupes, and Vichada maintain a trend towards very low PR. The PR trend is towards a marked decrease for the country, explained by the increase in extractive activities that compete with agricultural and livestock activities and the persistence of violent events such as forced displacement and land dispossession.

3.3. Socio-Ecological Resilience—SERII in 1985 and 2022

The spatiotemporal evolution of the SERII between 1985 and 2022 presents a particular distribution pattern, maintaining the proportions of lowest values (39.4% in 1985 and 39.4% in 2022) and highest values (30.3% in 1985 and 30.3% in 2022) constant over time (Figure 7, Table S4). The management score (MS) for the departments of Colombia was used to examine historical changes in the SERII from 1985 to 2022; some patterns can be identified, such as the tendency of La Guajira, Cesar, Vichada, Guaviare, and Vaupes to maintain the lowest values of socio-ecological resilience for this period. On the other hand, the departments of Guainía, Amazonas, Caquetá, Meta, Huila, Cauca, Boyacá, Arauca, Caldas, Santander, and Casanare (center of the country) exhibit the highest values of socio-ecological resilience between 1985 and 2022.
The MS allows one to identify priorities for sustainable management utilizing the rate of change in the SERII during the study period. This approach revealed that the MS for the departments of Colombia varied widely and responded to the particularities of each territorial unit.
In this sense, the highest priority for sustainable socio-ecological management in Colombia corresponds to the departments with the highest rate of temporal decrease in the SERII: La Guajira, Vaupés, Cesar, Vichada, and Guaviare.
The second priority for sustainable socio-ecological management corresponds to departments with the second highest rates of temporal decrease in the SERII; these are Sucre, Bolívar, Antioquia, Tolima, Córdoba, Norte de Santander, Nariño, Putumayo, Valle del Cauca, Cundinamarca, Santa Fe de Bogotá, and Choco.
In summary, according to the MS results, the SERII experienced a rapid decrease for 51.5% of the Colombian territory (Supplementary Materials, Table S5).

4. Discussion

4.1. Advantages and Disadvantages of the SERII for Colombian Political-Administrative Units Assessment

This research first explores the interrelations between subsystems with solid conflicts and tradeoffs in Colombia’s socio-ecological systems to prioritize areas (departments) for sustainable socio-ecological management based on the available information. In addition to the above, applying the SERII has demonstrated spatial sensitivity, reflecting changes in the resilience of diverse political-administrative units and with divergent levels of socio-environmental development.
The socio-ecological systems of megadiverse developing countries with a historical incidence of poverty, political corruption, armed violence, and political and social instability are highly complex. The proposals for a measure of resilience and socio-ecological resilience (SER) have overgrown in recent years [16,17,18]; many of these developments have focused on the formulation and empirical testing of indicators and indices for the socio-ecological resilience systems’ quantification [40,41,42,43,44,45,63,64]. However, many of these indicators do not involve multiple subsystems and factors for the spatiotemporal resilience measurement, which, although they may be in conflict, are crucial to understanding it in time and space [45,113].
According to Wu et al. [45], a large portion of the existing studies which have focused on SER have overlooked the contributions of additional subsystems, such as the social and productive systems, paying greater attention to the ecosystem structures and processes and not to social, economic, and productive structures. The SERII proposed for Colombia, evaluated with the available information (for 1985 to 2022), coincides with the in situ socio-environmental conditions throughout the country (Figure 7). Although the limitations of the availability of a historical series of socio-environmental information in Colombia are evident, this research points out the possibility of implementing comprehensive socio-ecological resilience indices to explore alternative ways to prioritize sustainable development actions in complex territories with limited economic and human resources, such as Colombia.
This approach, inspired by the work of Wu et al. [45] for coastal areas of China, uses remote sensing data and mapping socioeconomic parameters, which offer complete information on the spatial and temporal variations in SER, surpassing earlier studies that focused only on regional-scale valuations [38,42,134]. The proposed SERII index allowed for capturing, with a good level of detail, the spatial heterogeneity of the Colombian departments. However, this approach is subject to the scale effects, which could introduce uncertainty in the results due to the spatial heterogeneity of Colombia’s socio-ecological systems and multiple human activities [133]. To improve the SERII spatial accuracy, future research may consider incorporating more extensive and complete time series and even more precise parameters to assess SR, such as the available information on education evolution. The limitations on the available data are the central aspect that needs to be improved for future research. This study was addressed from 1985 to 2022, with available information that is relatively complete. Future developments can improve this assessment by incorporating additional parameters and considering the weights of the indicators based on the cultural and political context, as well as at more detailed spatial scales (e.g., detailed municipalities or biomes).
Another aspect of this proposal is that it provides an overview of SER in political-administrative management units, showing their capacity to withstand external disturbances rather than their responses to specific natural hazards or social events. Historically, the SER has been assessed in a fragmentary way. The most common individual indices are Social Vulnerability to Environmental Hazards (SoVI) [135], the Social Vulnerability Index (SVI) [136], Baseline Resilience Indicators for Communities (BRIC) [137], the Community Disaster Resilience Index (CDRI) [138], and the Resilience Capacity Index (RCI) [139]. These resilience and vulnerability indices have been widely assessed and benchmarked in the literature [140] and are used for case studies and within toolkits for various agencies [141,142,143]. Moreover, these indices cover multiple dimensions of SER, including vulnerability, social connectedness, and physical capital, obviously in individual form. Angeler and Allen [144] have demonstrated that SER is complex, and the interrelations between systems (ecological, social, and economic) are fundamental when it comes to approximating an accurate and operational calculation of it. For example, while extensive ecological protection can increase the ecological resilience of a system, it can hinder the development of social and production systems, weakening (in the medium term) ecological resilience and the resilience of all socio-ecological systems. Therefore, according to Wu et al. [45], this indicator can guide comprehensive sustainable management, considering the trade-offs between subsystems, which resemble a world with multiple conflicts of interest. Also, it is important to mention that applying a fuzzy comprehensive evaluation, entropy-based weights, and an analytical hierarchy process is critical to the objectivity of the SERII by determining, with minimal subjectivity, the weights of the indices used to evaluate the SER.
Finally, another limitation of this research is that the SERII is still a static SER analysis based on specific periods. Recent contributions, such as Sguotti et al. [145], point out that it is essential to understand resilience as a dynamic process to identify the presence of stable and unstable states in complex systems. These dynamic approaches to resilience assessment would provide probability estimates of a system crossing a tipping point characterized by hysteresis, assessing resilience about multiple external drivers and generating direct results for ecosystem-based management. Without a doubt, this approach seeks to understand resilience in a dynamic way that is more in line with the complex system’s reality. Future research should consider this approach. However, the information requirements and historical data series remain a limitation, which, at least for some countries, can be overcome by implementing comprehensive indices such as the SERII.

4.2. Violence and SER

The trajectory of a socio-ecological system also involves the history of the social, economic, and political systems; in this line, the existence and persistence of conflicts (armed, political, and social) are fundamental to understanding SER. Marginally, some contributions are reported in the literature involving the effect of violence (military, political, and social) on socio-ecological resilience [49,50,51,52,53,54,55,56], as well as the effect of equity and social justice on social resilience [57,58,59,60]. However, these studies remain preliminary and call for the contribution of more empirical evidence to understand this phenomenon.
For the specific context of Colombia, a country affected by a history of multiple forms of violence for more than 80 years, the effect of these violence forms on SER was evaluated for the first time in the literature. In this regard, the SERII results reveal that these violence parameters have a significant weight and influence on Colombia’s sociological systems. The results for 1895 and 2022 indicate that the departments with a higher historical incidence of military, political, and social violence present a weakening of their SER. These persistent phenomena of violence explain the historical persistence of low levels of SER in some regions of Colombia. Future research should consider political and historical aspects and parameters that describe the SER variations.

4.3. Implications of SERII for the Colombian Sustainable Management

According to this research, the departments of La Guajira, Cesar, Bolivar, Sucre, and Cordoba, in the Caribbean region of Colombia and Antioquia (between the Caribbean and Andean regions), present low SER values between 1985 and 2022. The following factors mainly explain this trend towards a low SER:
  • The Caribbean departments have experimented with fast changes in many ecosystems driven by economic development [146,147]. The most critical anthropogenic disturbances in Colombian Caribbean departments are mainly due to human population growth [148,149], changes in water quality [150], loss of natural vegetation cover [151,152], overfishing [153], tourism activities [154,155], sand mining [156], litter generation, and continental and/or alluvial mining [157,158]. Additionally, the persistence of poverty and sociopolitical violence in this region of the country are other threats to biodiversity and ER [159];
  • The Caribbean livestock departments (Córdoba, Magdalena, and Sucre) show an ecosystem transformation of more than 70%. A correlation between livestock activity and natural capital loss can be identified [160];
  • In the La Guajira, Cesar, Magdalena, and Bolívar (south) departments, important mining activities are conducted, represented by ferronickel exploitation (one of the most important worldwide) as well as coal (opencast) and gold mining, the latter at an artisanal and small-scale level using cyanide and Hg for the metal recovery. These economic activities are developed near the riverbeds and main tributaries, and in many cases, such as gold mining, in the rivers and stream currents, potentially contaminating the aquatic food chain [161];
  • In the case of the Antioquia department, the history of violence, the high degree of ecosystem transformation, the socioeconomic imbalances between urban and rural areas, and mining and illegal mining largely explain its low SER levels [65].
On the other hand, for the Guaviare and Guainía departments in the Amazon region, the rapid deforestation processes [162,163,164,165], habitat loss degradation [152,166], violence, illegal mining, and illicit crops are critical in the SER decreasing [65].
Finally, the calculation of the SERII determines that the departments of Boyacá, Cundinamarca, Valle del Cauca, Meta, and Santa Fe de Bogotá (center of the country) exhibit the highest values of socio-ecological resilience both in 1985 and 2022. According to Ruiz-Agudelo and Gutiérrez-Bonilla [83], these areas in the country’s center are located in territories where the natural capital loss amounted to more than 80% by 2024. However, these regions concentrate on the political and economic power of the country, which means that, driven by more significant SR and PR, these political-administrative units present stable tendencies towards greater SER. It is essential to note that these regions show a lower historical impact of military, political, and social violence.

5. Conclusions

This study adds to the knowledge about SER in two ways: (1) This paper presents a new case study of SER evaluation for a megadiverse developing country with a history marked by violence (political, military, and social), which is novel in the literature given the multiple diversity of methods and applications. (2) It presents an expanded methodological approach for evaluating SER with multiple indices, exploring the interactions between the ecological, social, and productive subsystems.
The SERII proposed for Colombia, evaluated with the available information (for 1985 to 2022), coincides with the in situ socio-environmental conditions throughout the country. This research shows that it is possible to use socio-ecological resilience indices to explore alternative ways to prioritize sustainable development actions in complex territories with limited economic and human resources, such as Colombia.
The MS-SERII (1985–2022) allows one to identify priorities for sustainable management utilizing the rate of change in the SERII during the study period. This approach revealed that the MS for the departments of Colombia varied widely and responded to the particularities of each territorial unit. The highest priority for sustainable socio-ecological management in Colombia corresponds to the departments with the highest rate of temporal decrease in the SERII: La Guajira, Vaupés, Cesar, Vichada, and Guaviare. The second priority for sustainable socio-ecological management corresponds to departments with the second highest rates of temporal decrease in the SERII; these are Sucre, Bolívar, Antioquia, Tolima, Córdoba, Norte de Santander, Nariño, Putumayo, Valle del Cauca, Cundinamarca, Santa Fe de Bogotá, and Choco. According to the MS results, the SERII experienced a rapid decrease in 51.5% of the Colombian territory.
For the specific context of Colombia, a country affected by a history of multiple forms of violence for more than 80 years, the effect of these violence forms on SER was evaluated for the first time in the literature. In this regard, the SERII results reveal that these violence parameters have a significant weight and influence on Colombia’s sociological systems. The results for 1895 and 2022 indicate that the departments with a higher historical incidence of military, political, and social violence shows a weakening of their SER.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17146461/s1, Table S1. Colombian Departments Ecosystem Resilience (ER). 1985-2022. Table S2. Colombian Departments Social Resilience (SR). 1985–2022. Table S3. Colombian Departments Production System Resilience (PR). 1985–2022. Table S4. Colombian Departments Socio-ecological Resilience Integrated Index (SERII). 1985–2022. Table S5. Management Score (MS) in Colombian departments. 1985–2022.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The author does not report any conflict of interest. The author Cesar Augusto Ruiz-Agudelo, is employed by RINA Consulting. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Study area.
Figure 1. Study area.
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Figure 2. Weight of parameters of subsystems (1985).
Figure 2. Weight of parameters of subsystems (1985).
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Figure 3. Weight of parameters of subsystems (2022).
Figure 3. Weight of parameters of subsystems (2022).
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Figure 4. Colombian departments’ ecosystem resilience (ER). (A) ER1985. (B) ER2022. (C) MS-ER1985–2022.
Figure 4. Colombian departments’ ecosystem resilience (ER). (A) ER1985. (B) ER2022. (C) MS-ER1985–2022.
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Figure 5. Colombian departments’ social resilience (SR). (A) SR1985. (B) SR2022. (C) MS-SR1985–2022.
Figure 5. Colombian departments’ social resilience (SR). (A) SR1985. (B) SR2022. (C) MS-SR1985–2022.
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Figure 6. Colombian departments’ production system resilience (PR). (A) PR1985. (B) PR2022. (C) MS-PR1985–2022.
Figure 6. Colombian departments’ production system resilience (PR). (A) PR1985. (B) PR2022. (C) MS-PR1985–2022.
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Figure 7. Colombian departments’ Socio-Ecological Resilience Integrated Index (SERII). (A) SERII1985. (B) SERIIOK2022. (C) MS-SERII between 1985–2022.
Figure 7. Colombian departments’ Socio-Ecological Resilience Integrated Index (SERII). (A) SERII1985. (B) SERIIOK2022. (C) MS-SERII between 1985–2022.
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Table 1. Parameters used in the socio-ecological resilience assessment for the Colombian departments (1985). + and − refer to the expected effect, positive or negative, on the sociological system.
Table 1. Parameters used in the socio-ecological resilience assessment for the Colombian departments (1985). + and − refer to the expected effect, positive or negative, on the sociological system.
SubsystemPrincipleParametersUnit+/−
Ecosystem resilienceEcosystem function [34,35,36] Natural capital availability (1985): the remnant natural capital after human intervention in each Colombian department [81,82,83,84]Int1985USD/ha/year+
Landscape diversity [20,38,44,85,86]Biome diversity: information from the 2017 Colombian continental, coastal, and marine ecosystems map (V.2.1) [87] to define and map Colombia’s biomes at 1:100,000 scaleScore (number of biomes per Colombian department)+
Ecological protection [29,39,88,89,90]Areas under legal protection in Colombia (1985): RUNAP [91] (https://runap.parquesnacionales.gov.co/) (Accessed on 3 October 2024)% (area under legal protection/total area of each Colombian department)+
Biodiversity loss [19,21,25,42,46,92]Natural cover loss (1985) [93]: MAPBIOMAS-Colombia (https://colombia.mapbiomas.org/) (Accessed on 5 October 2024)% (natural ecosystems loss area/total area of each Colombian department)−
Ecological stress [23,45,94]Natural capital loss (1985): the natural capital loss after human intervention in each Colombian department [81,82,83,84]Int1985USD/ha/year−
Social system resilienceDevelopment level [40,41,47,95,96]Total GDP (1985), per Colombian departments [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-departamentales) (Accessed on 5 October 2024)COP+
Economic support [30,31,98,99,100]Primary industry GDP (1985), per departments [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-departamentales) Accessed on 10 October 2024COP+
Cultural maintenance of natural conditions [42,43,44,101,102]Literacy rate (1985) [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/educacion) (Accessed on 20 September 2024).% +
Demographics [20,103,104,105,106,107] Population growth rate (1985) [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion) Accessed on 5 October 2024)Person/yr−
Regional military violence [50,51,55]War actions (1985): National Center for Historical Memory [108], Memory and Conflict Observatory (https://micrositios.centrodememoriahistorica.gov.co/observatorio/portal-de-datos/el-conflicto-en-cifras/acciones-belicas/) (Accessed on 3 October 2024)Score (total number of events by Colombian department)−
Events of armed violence against civilians [53,56,109]Population attacks (1985): National Center for Historical Memory [108], Memory and Conflict Observatory (https://micrositios.centrodememoriahistorica.gov.co/observatorio/portal-de-datos/el-conflicto-en-cifras/ataque-a-la-pobacion/) (Accessed on 10 October 2024)Score (total number of events by Colombian department)−
Poverty [57,58,59,60,110,111]Poverty (unmet basic needs—1985) [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/pobreza-y-condiciones-de-vida/necesidades-basicas-insatisfechas-nbi) (Accessed on 3 October 2024)%−
Production system resilienceProduction ability, diversity of agricultural and fishery products [33,112]Agricultural GDP (1985): total GDP (1985), per Colombian departments [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-departamentales) (Accessed on 10 October 2024)COP+
Natural condition [113,114]Biome diversity: information from the 2017 Colombian continental, coastal, and marine ecosystems map (V.2.1) [87] to define and map Colombia’s biomes at 1:100,000 scaleScore (number of biomes per Colombian department)+
Natural condition Loss [115,116,117]Extractive activities GDP (1985): total GDP (1985), per Colombian departments [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-departamentales) (Accessed on 3 October 2024)COP−
Production ability loss [53,54,56]Forced displacement (1985): Unidad de Víctimas del Conflicto armado del Gobierno de Colombia [118] (https://cifras.unidadvictimas.gov.co/Cifras/#!/infografia) (Accessed on 15 October 2024)Score (total number of events by Colombian Department)−
Production ability loss [49,52,53,54,56,60,119]Violent land dispossession (1985): Unidad de Víctimas del Conflicto armado del Gobierno de Colombia [118] (https://cifras.unidadvictimas.gov.co/Cifras/#!/infografia) (Accessed on 3 October 2024)Score (total number of events by Colombian department)−
Table 2. Parameters used in the socio-ecological resilience assessment for the Colombian departments (2022). + and − refer to the expected effect, positive or negative, on the sociological system.
Table 2. Parameters used in the socio-ecological resilience assessment for the Colombian departments (2022). + and − refer to the expected effect, positive or negative, on the sociological system.
SubsystemPrincipleParametersUnit+/−
Ecosystem resilienceBiological diversity [22,24,120,121,122,123]Total diversity (2022): map of life (https://mol.org/regions/) (Accessed on 10 October 2024)Score (total number of species reported by Colombian department)+
Landscape diversity [20,44,45,85,86]Biome diversity: information from the 2017 Colombian continental, coastal, and marine ecosystems map (V.2.1) [87] to define and map Colombia’s biomes at 1:100,000 scaleScore (number of biomes per Colombian department)+
Ecosystem function [34,35,36]Natural capital availability (2022): the remnant natural capital after human intervention in each Colombian department [81,82,83,84]Int2022USD/ha/year+
Ecological protection [29,39,88,89,90,124]Areas under legal protection in Colombia (2022): RUNAP (Single Registry of Colombian Protected Areas) [91] (https://runap.parquesnacionales.gov.co/) (Accessed on 15 October 2024)% (area under legal protection/total area of each Colombian department)+
Biodiversity loss [19,21,25,42,46,92]Natural cover loss (2022): Fundación Gaia Amazonas [93], MAPBIOMAS-Colombia (https://colombia.mapbiomas.org/) (Accessed on 3 October 2024)% (natural ecosystems loss area/total area of each Colombian department)−
Biodiversity loss [125,126]Endangered species (2022): map of life (https://mol.org/regions/) (Accessed on 3 October 2024)Score (total number of endangered species by Colombian department)−
Ecological stress [23,45]Natural capital loss (2022): the natural capital loss after human intervention in each Colombian department [81,82,83,84]Int1985USD/ha/year−
Social system resilienceDevelopment level [40,41,47,95,96]Total GDP 2022, per Colombian Department [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-departamentales) (Accessed on 15 October 2024)COP+
Economic support [30,31,98,100]Primary industry GDP (2022), per department [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-departamentales) (Accessed on 5 October 2024)COP+
Cultural maintenance of natural conditions [42,43,44,101,102]Literacy rate (2022) [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/educacion) (Accessed on 3 October 2024)% +
Economic support [98] Overcoming poverty (2022) [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/pobreza-y-condiciones-de-vida/pobreza-multidimensional) (Accessed on 3 October 2024)%+
Demographics [20,103,104,105,106,107]Population growth rate (2022) [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion) (Accessed on 3 October 2024)Person/yr−
Regional military violence [50,51,55]War actions (2022): National Center for Historical Memory [108], Memory and Conflict Observatory (https://micrositios.centrodememoriahistorica.gov.co/observatorio/portal-de-datos/el-conflicto-en-cifras/acciones-belicas/) (Accessed on 3 October 2024)Score (total number of events by Colombian department)−
Events of armed violence against civilians [53,56,109]Population attacks (2022): National Center for Historical Memory [108], Memory and Conflict Observatory (https://micrositios.centrodememoriahistorica.gov.co/observatorio/portal-de-datos/el-conflicto-en-cifras/ataque-a-la-pobacion/) (Accessed on 10 October 2024)Score (total number of events by Colombian department)−
Poverty [57,58,59,60,110,111]Multidimensional poverty (2022) [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/pobreza-y-condiciones-de-vida/pobreza-multidimensional) (Accessed on 10 October 2024)%−
Unemployment [37,106,127] Unemployment (2022) [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/mercado-laboral/empleo-y-desempleo) (Accessed on 3 October 2024)%−
Production system resilienceProduction ability, diversity of agricultural and fishery products [33,45,112]Productive units per department (2022) [97]: Encuesta Nacional Agropecuaria (https://www.dane.gov.co/index.php/estadisticas-por-tema/agropecuario/encuesta-nacional-agropecuaria-ena) (Accessed on 15 October 2024)Score (number of productive units per Colombian department)+
Social support [32,45]Population working in agricultural systems (2022) [97]: Encuesta Nacional Agropecuaria (https://www.dane.gov.co/index.php/estadisticas-por-tema/agropecuario/encuesta-nacional-agropecuaria-ena) (Accessed on 3 October 2024)%+
Production ability [33,45,112] Agricultural units with access to communications (2022) [97]: Encuesta Nacional Agropecuaria (https://www.dane.gov.co/index.php/estadisticas-por-tema/agropecuario/encuesta-nacional-agropecuaria-ena) (Accessed on 3 October 2024)%+
Social support [33,45,112] The professional workforce in agricultural units (2022) [97]: Encuesta Nacional Agropecuaria (https://www.dane.gov.co/index.php/estadisticas-por-tema/agropecuario/encuesta-nacional-agropecuaria-ena) (Accessed on 3 October 2024)%+
Natural condition loss [115,116,117]Extractive activities GDP (2022): total GDP (1985), per Colombian departments [97] (https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-departamentales) (Accessed on 10 October 2024)COP−
Natural condition loss [48,128,129] Access to poor-quality water (2022) [97]: Encuesta Nacional Agropecuaria (https://www.dane.gov.co/index.php/estadisticas-por-tema/agropecuario/encuesta-nacional-agropecuaria-ena) (Accessed on 3 October 2024). %−
Production ability loss [53,54,56]Forced displacement (2022): Unidad de Víctimas del Conflicto armado del Gobierno de Colombia [118] (https://cifras.unidadvictimas.gov.co/Cifras/#!/infografia) (Accessed on 3 October 2024).Score (total number of events by Colombian department)−
Table 3. Ranges for the indices for each class.
Table 3. Ranges for the indices for each class.
IndexVery LowLowMiddleHighVery High
ER<0.00.0–0.050.05–0.10.1–0.3>0.3
SR<0.00.0–0.060.06–0.10.1–0.3>0.3
PR<0.00.0–0.080.08–0.30.3–0.4>0.4
SERII<=0.00.0–0.20.2–0.40.4–0.6>0.6
MS-ER (1985–2022)<=−1.0−1.0–0.00.0–2.02.0–9.0>9.0
MS-SR (1985–2022)<=−1.0−1.0–0.00.0–2.02.0–9.0>9.0
MS-PR (1985–2022)<=−1.0−1.0–0.00.0–2.02.0–6.0>6.0
MS-SERII (1985–2022)<=−1.0−1.0–0.00.0–2.02.0–6.0>6.0
ER = ecosystem resilience. SR = social resilience. PR = production system resilience. SERII = Socio-Ecological Resilience Integrated Index. MS-ER (1985–2022) = management score for ecosystem resilience (1985–2022). MS-SR (1985–2022) = management score for social resilience (1985–2022). MS-PR (1985–2022) = management score for production system resilience (1985–2022). MS-SERII (1985–2022) = management score for Socio-Ecological Resilience Integrated Index (1985–2022).
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Ruiz-Agudelo, C.A. Proposal of a Socio-Ecological Resilience Integrated Index (SERII) for Colombia, South America (1985–2022). Sustainability 2025, 17, 6461. https://doi.org/10.3390/su17146461

AMA Style

Ruiz-Agudelo CA. Proposal of a Socio-Ecological Resilience Integrated Index (SERII) for Colombia, South America (1985–2022). Sustainability. 2025; 17(14):6461. https://doi.org/10.3390/su17146461

Chicago/Turabian Style

Ruiz-Agudelo, Cesar Augusto. 2025. "Proposal of a Socio-Ecological Resilience Integrated Index (SERII) for Colombia, South America (1985–2022)" Sustainability 17, no. 14: 6461. https://doi.org/10.3390/su17146461

APA Style

Ruiz-Agudelo, C. A. (2025). Proposal of a Socio-Ecological Resilience Integrated Index (SERII) for Colombia, South America (1985–2022). Sustainability, 17(14), 6461. https://doi.org/10.3390/su17146461

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