Empirical Social-Ecological System Analysis: From TheoreticalFramework to Latent Variable Structural Equation Model

Stanley Tanyi Asah

Received: 8 September 2006 / Accepted: 27 May 2008 / Published online: 5 September 2008

� Springer Science+Business Media, LLC 2008

Abstract The social-ecological system (SES) approach

to natural resource management holds enormous promise

towards achieving sustainability. Despite this promise,

social-ecological interactions are complex and elusive;

they require simplification to guide effective application of

the SES approach. The complex, adaptive and place-spe-

cific nature of human-environment interactions impedes

determination of state and trends in SES parameters of

interest to managers and policy makers. Based on a rig-

orously developed systemic theoretical model, this paper

integrates field observations, interviews, surveys, and latent

variable modeling to illustrate the development of simpli-

fied and easily interpretable indicators of the state of, and

trends in, relevant SES processes. Social-agricultural

interactions in the Logone floodplain, in the Lake Chad

basin, served as case study. This approach is found to

generate simplified determinants of the state of SESs,

easily communicable across the array of stakeholders

common in human-environment interactions. The approach

proves to be useful for monitoring SESs, guiding inter-

ventions, and assessing the effectiveness of interventions. It

incorporates real time responses to biophysical change in

understanding coarse scale processes within which finer

scales are embedded. This paper emphasizes the impor-

tance of merging quantitative and qualitative methods for

effective monitoring and assessment of SESs.

Keywords Social-ecological resilience �System indicators � Monitoring and assessment

Introduction

Natural resources management is also about managing

human claims and uses of nature (Gerlach and Bengston

1994). Human actions affect biophysical systems; bio-

physical change in turn affects humans and triggers human

responses that shape biophysical dynamics. There is a

surge of scholars and managers studying and managing

ecosystems and social systems as one system, that is,

linked or coupled human-environment systems (e.g.,

Gunderson and Holling 2002; Berkes and others 2003;

Clark and Dickson 2003; Folke 2006; Young and others

2006; Kotchen and Young 2007). Alternatively called

social-ecological systems (SESs), many theoretical models

of coupled human-environment systems have been pro-

posed (see Walker and others 2006a). One such model,

rigorously developed by Folke and others (2003), inte-

grates ecological, economic, cultural, sociopolitical, and

institutional dimensions of social-ecological interactions in

one coherent framework. The model embraces holism and

complexity, is rooted in empirical reality, and is commu-

nicated with metaphor and example (following Holling

2001). Understanding and managing for social-ecological

resilience (SER) is the primary goal of the model in which

SER depicts processes that create and/or enhance the

ability to cope with, adapt to, and shape changes in ways

that do not compromise future options for adaptability

(Folke and others 2003). This model, summarized in

Table 1, holds substantial promise in achieving sustain-

ability and is, thus, adopted for this study.

Due to the promise held by the SES approach, there is a

heightened need for simplified, easily interpretable and

communicable determinants of the state of, and trends in,

SES parameters relevant to managers and policymakers

(Carpenter and others 2001). Adoption of the SES

S. T. Asah (&)

Department of Forest Resources, University of Minnesota,

St. Paul, MN 55108, USA

e-mail: asah0002@umn.edu

123

Environmental Management (2008) 42:1077–1090

DOI 10.1007/s00267-008-9172-9

approach in practice has been hindered by the complex and

elusive nature of human-environment interactive processes.

These complex and elusive parameters are theoretically

constructed from a multiplicity of case-specific studies

(e.g., Berkes and Folke 1998; Walker and others 2006b).

Like other constructs, the parameters cannot be directly

observed and so do not lend themselves to direct methods

of measurement or direct means of measuring their degree

of presence. Moreover, social-ecological interactions are

place-specific; among similar biophysical environments,

the social, economic, cultural, and political contexts of

such interactions are different (Turner II and others 2003).

Contextual differences make it difficult to generate stan-

dard measures of parameters even among systems with

similar ecologies (Wiren-Lehr 2001). Because SES

dynamics are place-specific, management lessons from

processes in one appear less readily pertinent to others.

Another predicament with the SES approach is its focus

on coarse scales. Finer-scale human-environment interac-

tions, particularly at the level of individuals, are recognized

as key to sustainable management of human-dominated

systems (Westley 2002). Analyses of SESs focusing on the

individual decision maker, the resource management chal-

lenges he or she faces, the level of relationships in which he

or she is embedded and seeks to function, and the values,

norms, and perceptions informing interactions with nature

are rare, if not absent. The emphasis on one scale, partic-

ularly coarse scales (regions and/or large communities

involving long-term, slowly changing variables) inade-

quately reflect rate changes between biophysical and social

systems (MacMynowski 2007). For instance, global envi-

ronmental change manifests itself as short-term variance in

atmospheric conditions long before seasonal and annual

changes in mean conditions are observed. In the interim the

individual farmer, for example, is dealing with daily and

weekly fluctuations in precipitation and temperature

affecting everyday agricultural activities. A focus on coar-

ser scales within which this farmer is embedded potentially

misses out on finer scale real-time processes that aggre-

gately inform coarse-scale social responses to biophysical

change.

The goal of this paper is to validate Folke and others’

(2003) model for understanding SER and use the validated

model to determine the best employment of scarce farming

support resources in the Zina district of Cameroon. To

accomplish this goal, I use both qualitative and quantitative

methods (a) to simplify the complex SES paradigm to

empirically tangible information easily communicable to

varied stakeholders and (b) to bring finer scale social-

ecological interactions to bear on our understanding of

coarse scale processes. I adopt Folke and others’ (2003)

model as the basis for this analysis using a mix of field

observations, interviews, psychometric scale development

and administration through surveys, and latent variable

structural equation modeling (LVSEM). Psychometric

scales are one instrument used to measure complex,

Table 1 Tabular summary of Folke and others’ (2003) social-ecological system theoretical model

Management goal Primary processes: manifestations of SER Secondary processes: manifestations of primary processes/

secondary manifestations of SER

Acquire and/or enhance social-

ecological resilience by:

Dealing with change and uncertainty by: 1. Evoking disturbances such as fires in ways that mimic

natural disturbance patterns

2. Preparing for surprises

3. Learning from crises

4. Creating and sustaining social mechanisms for

participation and conflict resolution

Nurturing diversity for reorganization and

renewal by:

1. Nurturing ecological memory

2. Sustaining social memory

3. Enhancing social-ecological memory

Combining different types of knowledge for

learning by:

1. Combining experiential and experimental knowledge

2. Expanding from knowledge of structure to knowledge of

function

3. Building process knowledge into institutions

4. Fostering the complementarity of different knowledge

systems

Creating opportunities for self-organization

toward sustainability by:

1. Recognizing the interplay between diversity and

disturbance

2. Dealing with cross-scale dynamics

3. Matching scales of ecosystem and governance

4. Accounting for external drivers of change

1078 Environmental Management (2008) 42:1077–1090

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dynamic, and often intangible theoretical phenomena

(DeVellis 2003). The method of LVSEM is one used to test

assertions of the possibility and nature of interrelationships

among theoretical phenomena as well as relationships to

the measures assessing these phenomena (Maruyama 1998;

Raykov and Marcoulides 2000; Pugesek 2003). A key

aspect of LVSEM is the distinction between latent vari-

ables (hypothetically existing theoretical phenomena such

as SER) and manifest variables (those actually measured

by the researcher). That distinction enables empirical

simplification of SES parameters by recasting them as

latent variables with indicators. Correlation coefficients

between latent variables and indicators were estimated.

The following sections briefly explain how the SES

theoretical model proposed by Folke and others (2003) is

presented as a LVSEM. Then the study area and partici-

pants are presented, followed by a description of how

data were collected and used to estimate relationships

among model parameters. Results show that the complex-

ity of SESs can be simplified to easily interpretable and

communicable determinants of the state of SESs. An

illustration of how determination of system state facilitates

the location of leverage points for management interven-

tion is presented. I show how aggregate real-time social

responses to biophysical change are incorporated in

understanding coarse-scale social-ecological interactions.

The utility of this approach for both monitoring and

assessment of SESs is discussed. The paper demonstrates

how, from theoretical models, simplified systemic indica-

tors of SES parameters can be generated to enhance our

understanding of a wide variety of SES configurations.

From Theory to Latent Variable Structural Equation

Model

In a synthesis of numerous empirical SES analyses, Folke

and others (2003) proposed a rigorously developed and

adequately integrative SES theoretical model. Managing

for SER is a major goal of Folke and others’ model; SER is

defined as the ability to cope with, adapt to, and shape

change in ways that do not compromise future options for

adaptability. The model (Table 1) suggests that SER is

manifested through four key processes. Although we can-

not directly measure SER, the model proposes these four

processes as primary manifestations (indicators) of SER

(following Meadows 1998 and Bossel 1999). That is, a SES

is resilient to the degree that it adequately manifests these

processes. Therefore, we can acquire insights about the

resilience of a SES by examining its relationship with these

processes. The more adequate these processes are, the more

resilient a system is. That is, we expect a positive rela-

tionship between these processes and SER; the stronger the

relationship, the better the process. Similarly, each of these

four key indicators of SER manifests itself through sec-

ondary processes (Table 1). For example, a SES adequately

deals with change and uncertainty to the degree that it (i)

evokes disturbances in ways that mimic natural patterns,

(ii) prepares for surprises, (iii) learns from crises, and (iv)

engages in social mechanisms for participation in resource

management and conflict resolution. Correspondingly, we

can gain insights about each of these primary processes by

examining their relationship with respective secondary

processes.

The theoretical model presented as a LVSEM in Fig. 1

leaves us with a two-order string of primary and secondary

indicators of SER amenable to causal inference (Salmon

1998; Bossel 1999; Pearl 2000). This two-order theoretical

model of human-environment dynamics sets the stage for

empirical analysis of SESs using a mix of qualitative and

quantitative methods at the center of which is LVSEM

(Creswell 2003). In Fig. 1, the ovals represent latent (the-

oretical constructs of processes) variables, while the

rectangles each represent the aggregate scores of a scale

constituting manifest variables. Figure 1 is a version of

LVSEM called a second-order confirmatory factor analysis

(CFA) model. A CFA model is a latent variable measure-

ment model examining relationships between latent

variables and their respective manifest variables (Bar-

tholomew and Knott 1999). The second-order refers to the

primary and secondary indicators; primary processes indi-

cate SER while secondary processes indicate primary

processes, making it a second-order CFA model of SER. In

this second-order CFA of SER, correlation coefficients

between latent variables and indicators can be estimated in

two stages: first, between primary processes as latent

variables and secondary processes as indicators and, sec-

ond, between SER as latent variable and primary processes

as indicators (Hoyle and Smith 1994; Cheung 2000; Pug-

esek 2003). Consequently through model estimation and

interpretation, these relationships are discerning of primary

processes and, ultimately, SER.

Approach

Four villages in the district of Zina, within the semiarid

Logone floodplain in Cameroon within the Lake Chad

basin, were chosen for this study, for two major reasons.

First, it is historically subjected to ecological stresses due

to extreme hydrologic variability characterized by droughts

and unpredictable rainfall patterns. Second, cultural edges

exist due to the active presence of about 75 distinct cultural

communities relying on farming, fishing, and livestock

breeding for their livelihoods (Mouafo and others 2002;

Turner and others 2003). These cultural edges provide

Environmental Management (2008) 42:1077–1090 1079

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ample range of traditional ecological knowledge from

which to draw in times of stress and change that typify the

area. The area is thus a good place to learn about real-time

social responses to biophysical change and thence to

empirically validate the SES model. The major subsistence

activity is agriculture, with rice the most produced and

marketed product. Farm plots are owned by individuals

rather than households, making individual-agricultural

interactions self-sustaining SESs manifesting similar attri-

butes as the coarse-scale communities and regions within

which they are embedded. The study was reviewed and

approved by the University of Minnesota Institutional

Review Board. Participants, village chiefs, and community

elders were informed of the nature of the study and their

rights to decline and/or cease participating at any point

during the study.

Figure 2 is a schematic outline of the research proce-

dure. Participants were local paddy rice farmers 18 years

and older of both genders, who owned and farmed a paddy

rice field for at least a year prior to this study. These

individuals influence and respond to biophysical changes

through farm practices and social mechanisms (processes)

informed by traditional knowledge, beliefs, and values as

well as political and socioeconomic factors. The essence of

SER: Key Parameter

Primary Processes/Indicators of SER

Secondary Processes/Indicators of Primary Processes

Social-Ecological Resilience

Knowledge of Structure toKnowledge of Function

Building Process Knowledge into Institutions

Fostering Knowledge Complementarity

Combining Experiential & Experimental Knowledge

Combining different types of

knowledge for learning

Sustaining Social Memory

Enhancing Social-EcologicalMemory

Nurturing Ecological Memory

Nurturing diversity for reorganization

& renewal

Dealing with Cross-Scale Dynamics

Matching Scales of Ecosystem & Governance

Accounting for External Drivers of Change

Interplay Between Diversity & Disturbance

Creating opportunities for self-organization

Preparing for Surprises

Learning from Crises

Mechanisms for Participation & Conflict Management

Evoking Disturbances

Dealing withchange and uncertainty

Fig. 1 Second-order

confirmatory factor analysis

model showing primary and

secondary indicators of social-

ecological resilience. Ovals

represent latent variables and

rectangles represent manifest

variables; the arrows are

estimable correlation

coefficients

1080 Environmental Management (2008) 42:1077–1090

123

social-ecological dynamics lies in these practices, mecha-

nisms, beliefs, knowledge, values, and political and

socioeconomic factors, which are grouped into four pri-

mary and respective secondary processes within Folke and

others’ (2003) model. Psychometric scales were developed

and used, through survey questionnaires, to measure sec-

ondary processes at the level of individuals (Spector 1992).

Using LVSEM techniques, the relationships between pri-

mary and secondary processes and between SER and

primary processes were estimated. In the following section,

I describe the process of psychometric scale development,

data collection, and use of LVSEM to estimate relation-

ships among SES parameters.

Psychometric Scale Development and Data Collection

The outlined secondary processes are social-ecological

theoretical constructs (Cronbach and Meehl 1955). That is,

they are postulated parameters of SESs that can be mea-

sured using psychometric scales (Noar 2003). These

constructs are about social-ecological practices, values,

behaviors, attitudes, knowledge, and perceptions; they

are dynamic, intangible, elusive, and multidimensional.

Psychometric scales (questionnaires with ordinal and/or

quantitative response ratings) are one instrument used to

measure complex, dynamic, and intangible theoretical

phenomena (DeVellis 2003). The psychometric scales were

Summated Rating Scales (following Spector 1992), i.e., the

sums of ratings of constituent items within the scales rep-

resent an aggregate score for the process that the scale is

designed to measure.

Prior to field observations and based on background

knowledge of the study area, a questionnaire comprising 15

pilot psychometric scales was developed to match the 15

secondary SES processes outlined in the theoretical model

(see Table 2 for sample psychometric scale items). Pilot

scales contained multiple items designed to reveal levels of

the various essences of secondary processes and thus to

assemble varied pieces of information about these pro-

cesses. As described in the theoretical model, scales

contained items that revealed levels of economic, political,

cultural, and ecological dimensions of secondary processes.

Scales were designed such that respondents could rate

multiple items about social-agricultural processes as well

as their knowledge, beliefs, and attitudes regarding these

processes. Given the multidimensional nature of SES pro-

cesses, a minimum of 12 items was included in each scale:

Development of psychometric scales based on theoretical model and prior knowledge of study area

Review of scales by academic practitioners in the broad field of human-environment

interactions & measurement theory

Focus groups, key informant interviews, participant observations & pilot survey

Scales reviewed based on reviews, interviews, field

observations & pilot survey

Administration of modified scales, assembled as a questionnaire, to a random sample of population

Analyses of scales for social desirability & internal consistency

reliability using SPSS software

Confirmatory factor analyses of primary processes using summed

ratings of scales as covariance matrix inputs using LISREL software

Confirmatory factor analyses of resilience using sum of indicators

of primary processes as covariance matrix inputs using LISREL

Interpretation and reporting of results with the aid of field notes

& interview transcripts

Fig. 2 Flowchart of study

procedure: LISREL (Linear

Structural Relations) is a

structural equation modeling

software program

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twice the number anticipated to be retained after scale

analysis (following Noar 2003). Items were written fol-

lowing guidelines for writing clear, unambiguous items

(Converse and Presser 1986).

Most items in the pilot scales were in the Likert-scale

format with five response options: (1) strongly disagree, (2)

disagree, (3) neutral, (4) agree, and (5) strongly agree. Fre-

quencies of occurrence of events were rated in five response

options: (1) never, (2) rarely, (3) sometimes, (4) almost

always or almost every year, and (5) always or every year.

Scales also contained items with other scores such as dura-

tion (years, months, weeks) and numbers (e.g., of years,

people, things, activities, distances). Most items were posi-

tively worded such that higher ratings indicate higher

affirmations of the particular process being measured. Social

desirability, the tendency to alter responses so as to avoid

unfavorable presentation of the self, can negatively affect

the accuracy of psychometric scales (Tourangeau and others

2002). To assess and correct for social desirability, eight

items from the short form of the Marlowe-Crowne Social

Desirability Scale (MCSDS) (Fischer and Fick 1993) were

adapted and included in the questionnaire (following DeV-

ellis 2003). Items prone to social desirability are eliminated

by comparing correlations of psychometric scale items to

those of the social desirability scale.

To ensure content and construct validity, scales were

reviewed by academic practitioners in psychometric mea-

surement and within the broad area of human-environment

interactions and modified as relevant (Converse and Presser

1986). To ensure context specificity and incorporate local

ecological knowledge, I observed farm practices and group

activities related to farming and conducted key informant

and focus group interviews (Weiss 1995; Krueger and

Casey 2000). Interview participants were selected based on

different levels (years) of experience with farming and

related social-ecological processes within the Logone.

Because most participants could speak only the Mousgoum

and/or Kotoko native languages, interviews and observa-

tions were facilitated by formally educated natives of the

Mousgum and Kotoko tribes. Facilitators had prior

research experience in the area and helped recruit partici-

pants and translated/interpreted conversations during

interviews. The facilitators also administered surveys

through personal interviews that allowed for the translation

of survey questions to respondents. Interviews and obser-

vations focused on contextual relevance and narratives

about social-ecological system processes outlined in the

proposed model. These contextually relevant responses

were used to revise survey instruments to ensure that the

contents merged the theoretical framework with local

social-ecological knowledge of participants. Issue analysis

of narratives helped in explanation of the qualitative details

of model outputs (Weiss 1995). The modified scales were

administered to a pilot sample of farmers within the

research area to further ensure content validity and estab-

lish face validity. Respondents were asked to choose the

ratings that best fit their agreements or endorsements of the

statements (Alreck and Settle 1995).

After the pilot survey, measures were taken to minimize

cultural nuances, facilitate comprehension, and boost

response rates. The wording of items within scales and the

number of response options were discussed with pilot

survey participants (Foddy 2001; Tourangeau and others

2002). Items with five response options were deemed one-

too-many options and thus potentially confusing. Some

respondents also viewed the neutral response option as

expression of lack of concern, rather than indifference,

with the issue being assessed. These and other findings

from the pilot survey and discussions with pilot survey

participants were used to further revise scales. The number

of response options for Likert-scale formatted items were

reduced to four: (1) strongly disagree, (2) disagree, (3)

agree, and (4) strongly agree. Frequencies of occurrence of

events were rated (1) never, (2) sometimes, (3) almost

always or almost every year, and (4) always or every year.

As shown in the results, reduction in the number of

response options did not preclude attainment of precise

measures of secondary SES processes. The farmers were

Table 2 Examples of response options for scale items assessing secondary social-ecological system processes

Sample secondary social-ecological processes Sample scale items Response options

Preparing for surprises How long does your harvest last on average? Months

Besides farming, in how many lucrative activities are you engaged? Number of activities

Nurturing ecological memory I grow N rice varieties that were grown here even

before the last drought.

Number of varieties

Rules regarding the use of community forest are well respected. Agreement

Combining experiential and experimental

knowledge

I participate in agricultural demonstrations organized by NGOs. Frequency

What is your level of education? Number of years

Recognizing the interplay between diversity

and disturbance

It is good to grow multiple varieties of rice every year. Agreement

How often do you take measures to prevent wild fires? Frequency

1082 Environmental Management (2008) 42:1077–1090

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thus, extensively involved in defining site-specific social-

agricultural realities and shaping the content (choice and

phrasing of items) of the scales.

Scales were then assembled into a survey questionnaire

and administered to a purposeful random sample (Kalton

1983) of 260 paddy rice farmers in the villages of Gala,

Lahay, Padmangay, and Araynaba. The complete ques-

tionnaire comprised 250 questions; a section of 84–90 items

was administered, at a time, to each individual through

personal interviewing. Incomplete surveys, due to unavail-

ability of respondents for subsequent interviews, were

eliminated resulting in 201 usable surveys for this analysis.

Psychometric Scale Analyses

In the returned surveys, the scale measuring each second-

ary process was assessed for social desirability and

precision of measurement (internal consistency reliability).

Items deemed liable to social desirability were included in

an SPSS spreadsheet of the MCSDS entries; individual

item-total item correlations were examined and those that

correlated strongly (C0.51) with the MCSDS items were

eliminated (following DeVellis 2003). SPSS (9.0) was used

to assess internal consistency reliability of scales by

examining Cronbach’s (1951) coefficient alpha. Cron-

bach’s alpha, like other measures of precision, is the

proportion of the scale total variance that is attributable to

the true score of the process underlying the items within

that scale (DeVellis 2003). Coefficient alpha analyses

involve examining the means and variance of items as well

as inter-item correlations. Alpha analyses inform the ana-

lyst of the effect of excluding each item on the overall

scale’s reliability and suggest scores of items to be

reversed. Items were eliminated and scores reversed based

on these observations, and scales were reanalyzed until an

acceptable precision (a C 0.6 following DeVellis [2003])

was achieved. Thus, high ratings (i.e., high frequencies and

agreements) do not necessarily affirm the process being

measured until it is proven so via internal consistency

analysis. Once an acceptable alpha was achieved, the scales

were further scrutinized for redundancy by eliminating

items deemed similar to others as long as elimination did

not compromise measurement precision (following DeV-

ellis 2003). Items retained within each scale represented

acceptable internally consistent estimates of the underlying

secondary SES processes they sought to measure.

CFA Model Estimations

Latent variable structural equation modeling uses linear

algebraic equalities to estimate regression coefficients

between measured and unmeasured (latent) processes while

accounting for error in the representation and measurement

of those processes (Maruyama 1998; Pugesek and others

2003). The maximum likelihood method was used to

estimate CFA models because the variables used as input

(sum of scales and sum of indicators of measurement

models) showed mild departures from normality with

absolute univariate skewness and kurtosis of B1.0.

Although the maximum likelihood estimation assumes

multivariate normality, its parameter estimates are known

to be robust to mild to moderate departures from normal

distributions (Fouladi 2000; Nevitt and Hancock 2001).

Models were estimated in two stages. In the first stage, four

models were estimated; each of the four primary processes

was modeled as a latent variable with respective secondary

processes as indicators. In the second stage, a fifth model

was estimated, with SER as the latent variable and primary

processes as indicators.

Stage 1: Models of Primary Processes

Item ratings for each scale measuring a secondary process

were summed into a metric-free index of that process

(Spector 1992; Bossel 1999; Zhen and Routray 2003). For

each of the four primary processes, a covariance matrix of

respective metric-free indexes was used as input into the

Linear Structural Relations (LISREL version 8.72) soft-

ware syntax window (Joreskog and Sorbom 2005). Desired

relationships were specified allowing metric-free indexes

of secondary processes to be modeled as functions of their

respective primary processes plus random (residual) error

using the system of Eq. 1 (du Toit and du Toit 2001).

Yi ¼ bi f þ di ð1Þ

In Eq. 1, Yi is the sum of ratings within scales measuring

respective secondary processes, f is the primary process

(latent variable), bi is the regression coefficient between Yi

and f, and di is the residual error. Given a covariance matrix

of Yi values, LISREL simulates values for f, which are used

to estimate bi’s and di’s. Because f is a latent variable

(unmeasured construct) and takes the same set of simulated

values in this system of equations, LISREL automatically

sets the metric of its latent scale, usually to 1 (Raykov and

Marcoulides 2000). Model outputs from LISREL include

visually explicit graphical expressions of specified

conceptual relationships.

Stage 2: Model of Social-Ecological Resilience

Each acceptably fitted CFA model of primary processes

constitutes a measurement model for that process. Thus,

aggregate (sum or average) scores of their indicators rep-

resent a metric-free measure of that process (Maruyama

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1998). In this study, there exists a higher-order latent

variable (resilience) with multiple indicators (primary

processes) simultaneously observed using the same meth-

ods (survey instruments). Consequently, the variance of

aggregate scores of indicators of primary processes can

be used to estimate a correlation coefficient between pri-

mary processes and SER (see Cheung 2000). A fifth CFA

model, depicting primary processes as indicators of SER,

was then estimated. In the model for each primary process,

the scores of its indicators were summed to represent a

metric-free index of that primary process. The mean of

these scores was shown to have the same results. A

covariance matrix of all four metric-free indexes (Yi’s) of

primary processes was used as LISREL input. Using the

same system of Eq. 1, Yi values are the metric-free indexes

for primary processes; f is SER, values for which are

simulated by LISREL based on the values for indices of

primary processes; bi values are the regression coeffi-

cients between Yi’s and SER; and di values are the residual

errors.

All CFA models were tested for their absolute and rel-

ative fit to data and interpreted in the context of system

state. The relative strengths of indicators of primary pro-

cesses and of SER were judged using the concept of

indicator strength (Hyman and Leibowitz 2001). Equation

2 was used to obtain the Index of Indicator Strength (IIS)

for respective processes.

IIS ¼ b2ð1� dÞ ð2Þ

In Eq. 2, b2, the square of the correlation coefficient, is

the coefficient of determination of indicators, and 1 – d is

the common or true score variance.

The fit (adequacy) of each estimated CFA model was

gauged using two fit indexes: the standardized root mean

square residual (SRMR) and the Comparative Fit Index

(CFI). The SRMR is an absolute index used to judge

whether the unexplained variance after model fitting jus-

tifies rejecting the model. The SRMR was chosen for its

high sensitivity to model misspecification. Models with

SRMR values of B0.08 are considered acceptable (Hu and

Bentler 1998). The CFI is a relative index that judges how

well a particular model explains a set of observed data

compared with other possible sets of models (Maruyama

1998). Models with values of CFI C0.90 are considered

acceptable (Bentler and Bonnett 1990). Three of the five

models yielded improper solutions in which standardized

factor loadings were slightly [1 in absolute value and/or

standard errors insignificantly\0. Improper factor loadings

were constrained to 1, and standard errors to 0 (following

Chen and others 2001; Dillon and others 1987), to correct

for these cases. These corrections did not interfere with

inferences from the models; constraints did not raise any

model identification problems.

Results

The results (Table 3) consist of the precision (coefficient a)

of the scales used to measure each secondary process,

outputs of CFA models showing correlation coefficients

between primary processes and respective secondary pro-

cesses, and indexes of indicator strengths (IIS) of each

secondary process. Scales measuring all 15 secondary

processes were acceptably precise or internally consistent

(DeVellis 2003). All four CFA models of primary pro-

cesses and the fifth model of SER fit well (Table 4).

Models of Primary Processes

For the four primary processes, relationships between each

and its respective secondary processes were estimated

(Table 3). All four secondary processes, employed to deal

with change and uncertainty, had significant (t-values[2.0)

influences on this primary process. The secondary process

(practices and social mechanisms) through which farmers

prepare for crises was the strongest indicator (IIS = 0.35) but

had a negative impact (b = -0.77) on the farmers’ ability to

deal with change and uncertainty. All three secondary pro-

cesses had significant influences on the practices and social

mechanisms employed to nurture diversity for reorganiza-

tion and renewal. The same is true for the processes adopted

to create opportunities for self-organization. Processes

employed to expand from knowledge of structure to that of

function and to build process knowledge into institutions did

not significantly influence efforts to combine different types

of knowledge for learning. Nurturing ecological memory was

the most influential process in efforts to nurture diversity for

reorganization and renewal. Processes through which the

farmers recognize and accommodate the interplay between

diversity and disturbance had the strongest, but negative,

influence (IIS = 0.39, b = -0.79) on farmers’ ability to

create opportunities for self-organization. However, pro-

cesses adopted to deal with cross-scale dynamics and

accounting for external drivers of change had almost-equal

strengths (ISS = 0.30 and ISS = 0.35, respectively) in their

positive influences (b = 0.74 and b = 0.77) on self-orga-

nizational processes. Practices and social mechanisms that

foster complementarity of different knowledge systems had

the strongest, but negative, influence (b = -1.00, IIS =

1.00) on processes aimed at combining different types of

knowledge for learning. Complete results for the four models

of primary processes are shown in Table 3.

Model of Social-Ecological Resilience

Figure 3 shows the SER model (model 5), an example of a

graphical output of CFA model, displaying relationships

between SER as a latent variable and primary processes as

1084 Environmental Management (2008) 42:1077–1090

123

indicators. All four primary processes were significant

(t-values C2.0, the default LISREL cutoff) determinants of

the farmers’ resilience, i.e., ability to cope with, adapt to,

and shape change in ways that does not compromise future

options for adaptability. That ability is most significantly

influenced (b = 1.00, IIS = 1.00) by the practices and

social mechanisms employed by Logone farmers in creat-

ing opportunities for self-organization toward sustain-

ability. Efforts to combine different types of knowledge

for learning are having a negative effect (b = -0.17) on

system resilience.

Interpretation and Implication of Results

For each model, the IIS points to the importance of a given

process and the sign of its coefficient, b, suggests how well

that process fares and help guide management and policy

interventions. In ideally resilient systems and without

measurement error, a value of 1.00 is expected for every band IIS. It is evident in the overall model for SER (Fig. 4)

that the resilience of this system is more influenced by self-

organizational processes (IIS = 1.00) than by efforts to

deal with change and uncertainty (IIS = 0.0004), nurture

diversity for reorganization (IIS = 0.07), and combine

different types of knowledge for learning (IIS = 0.001).

The complexity of these processes was reduced to corre-

lation coefficients and indexes of indicator strengths that

are insightful to our understanding of social-ecological

processes within the study area.

As shown in the CFA model for SER, practices and

social mechanisms geared toward combining different

types of knowledge for learning negatively influenced

(b = –0.17) the system’s resilience. In the usual circum-

stances of limited resources for intervention, it would be

Table 3 Precision of measurement, coefficient alpha (a), correlation coefficients (b), and indexes of indicator strength (IIS) for secondary

processes

Primary processes Secondary processes: indicators

of primary processes

Precision of

measurement

coefficient a

Correlation

coefficient (b)

Index of

indicator

strength (IIS)

Model 1: dealing with change

and uncertainty

Evoking disturbances 0.58 0.63 0.16

Preparing for surprises 0.71 -0.77 0.35

Learning from crises 0.83 0.18 0.001

Social mechanisms that enhance learning 0.81 0.61 0.14

Model 2: nurturing diversity for

reorganization and renewal

Nurturing ecological memory 0.83 1.00 1.00

Sustaining social memory 0.80 0.49 0.06

Enhancing social-ecological memory 0.83 0.70 0.25

Model 3: combining different types

of knowledge for learning

Combining experiential and experimental knowledge 0.76 -0.60 0.13

Expanding from knowledge of structure

to knowledge of function

0.77 0.07* 0.0001*

Building process knowledge into institutions 0.85 0.09* 0.0001*

Fostering the complementarity of different

knowledge systems

0.83 -1.00 1.00

Model 4: creating opportunities

for self-organization

Recognizing the interplay between diversity

and disturbance

0.82 -0.79 0.39

Dealing with cross-scale dynamics 0.60 0.74 0.30

Matching scales of ecosystem and governance 0.71 0.56 0.10

Accounting for external drivers of change 0.83 0.77 0.35

* Insignificant (t-value \2.0) indicators

Table 4 Fit statistics of all five CFA models: standardized root mean square residual (SRMR) values B0.05 and comparative fit index (CFI)

values C0.90 are acceptable

CFA model SRMR CFI df v2

Learning to live with change and uncertainty 0.007 1.00 2.00 0.25

Nurturing diversity for reorganization and renewal 0.005 1.00 1.00 0.09

Combining different types of knowledge for learning 0.028 1.00 4.00 3.66

Creating opportunities for self-organization toward sustainability 0.041 0.96 2.00 17.68

Social-ecological resilience 0.047 0.94 3.00 8.34

Environmental Management (2008) 42:1077–1090 1085

123

sensible to allocate resources toward improving the farm-

ers’ ability to combine different types of knowledge for

learning, given its negative manifestation in the resilience

model. That is, this could be the place to intervene to

enhance the farmers’ overall ability to cope with, adapt to,

and shape changes in ways that do not compromise future

options for adaptability. The resilience model thus directs

our attention to particular primary processes. Once the

primary processes with leverage (combining different types

of knowledge for learning) are identified, a CFA model(s)

of those processes can be further examined. The CFA

model combining different types of knowledge as latent

variables and its respective secondary processes as indi-

cators is shown in Fig. 4. The results show that efforts to

foster the complementarity of different knowledge systems

had the strongest (IIS = 1.00), but negative (b = -1.00),

influence on the processes adopted to combine differ-

ent types of knowledge for learning. The secondary

process adopted to combine experiential and experimental

knowledge had the second strongest (IIS = 0.35), but

negative (b = -0.60), influence on the primary process of

combining different types of knowledge for learning. These

two secondary processes with negative influences would be

the place to intervene to render the farmers’ initiatives to

combine different types of knowledge more effective and

efficient. Thus the two-stage model estimations, using the

magnitude and signage of coefficients (b and ISS values),

help guide the need to intervene for management and

policy, and where.

Once leverage points for intervention, at the level of

secondary processes, are identified, detailed qualitative

analysis shedding more light on these social-ecological

processes is possible. The psychometric instruments mea-

suring secondary processes contained items assessing

various aspects of these processes as informed by the

theoretical model, field observations, and the farmers

themselves via interviews. Thus, constituent items within

each instrument, together with notes from field observa-

tions and interviews, are useful in the detailed explanation

of processes needy of intervention. In the case of com-

bining different types of knowledge for learning, I use

instruments, field notes, and interview transcripts to briefly

examine the two secondary processes with strong but

negative influences on efforts to combine different types of

knowledge for learning. According to survey instruments,

the average farmer had ample experiential knowledge,

having lived (mean, 37 years) and farmed (mean 21 years)

in the area for extended periods of time. Through inter-

views, it was revealed that the major source of farmers’

exposure to experimental (formal agricultural) knowledge

is through extension services and formal education. Anal-

ysis of the instrument for combining different types of

knowledge for learning shows that farmers had, on average,

less than 4 years of formal education, and more than 90%

of them had never had contact with an agricultural exten-

sion agent. Thus instruments and notes from field

observations and interviews point to the model’s sugges-

tion that farmers lack sufficient experimental knowledge to

complement their experiential knowledge base.

Substantial efforts, by the farmers, to foster the com-

plimentarity of different knowledge systems were

observed. They participated in labor cooperatives and

related group activities. These groups were segregated

based on gender; male groups tended to comprise every age

bracket, while younger and older females distinctly

belonged to separate groups. As explained earlier, most of

these farmers were not formally educated and had never

been exposed to any form of agricultural extension service;

there is little experimental knowledge transfer within

groups. In addition, the age and gender separation in farm

groups impedes transmission of traditional ecological

(experiential) knowledge among community members.

1.00 (1.00)

- 0. 17 (0.001)

0. 52 (0.07)

0. 15 (0.0004)

Social-EcologicalResilience

Dealing with change & Uncertainty

Nurturing diversity for re-organization & renewal

Combing different types of knowledge for learning

Creating opportunities for self-organization

Fig. 3 Visual output of confirmatory factor analysis model of social-

ecological resilience. The figures on the arrows are correlation

coefficients; indexes of indicator strengths are shown in parentheses.

Model fit statistics: standardized root mean square residual (SRMR)

= 0.047; Comparative Fit Index (CFI) = 0.94

From knowledge of structure to knowledge of function

Building process knowledge into institutions

Fostering the Complementarity of different knowledge

systems

Combining experiential and experimental knowledge

-1.00 (1.00)

0.09* (0.0001)

0. 07* (0.0001)

-0.60 (0.13)

Combing different types of

knowledge for learning

Fig. 4 Confirmatory factor analysis model of processes adopted to

combine different types of knowledge for learning. The figures on the

arrows are correlation coefficients; indexes of indicator strengths are

shown in parentheses. Asterisks represent insignificant indicators.

Model fit statistics: standardized root mean square residual

(SRMR) = 0.028; Comparative Fit Index (CFI) = 1.00

1086 Environmental Management (2008) 42:1077–1090

123

These qualitative findings explain the strong (IIS = 1.00)

but negative (b = -1.00) influence of their efforts to

complement different knowledge systems. Without ade-

quate avenues to combine and complement different

knowledge systems, it is not surprising that efforts to

expand from knowledge of structure and to build process

knowledge into institutions are ineffective (insignificant

b = 0.07 and b = 0.09, respectively; Fig. 4). Thus,

besides determining leverage points for intervention, this

approach closes knowledge gaps by guiding detailed

qualitative analysis for enhancing social-ecological

resilience.

Discussion

Besides determining leverage points for intervention, and

orienting the focus of the analyst, this study’s approach

incorporates variance in finer-scale responses to ecological

change in understanding coarse scales embedding these

finer scales. The outcomes are for the community and/or

villages as integral wholes but rely on the variance of

observations made at the level of individuals. At this level,

real-time responses to ecological change were captured.

Constituent items within instruments allowed assessments

of various processes involved in the individual resource

manager’s interaction with nature. Through aggregate

variance of these real-time responses, coarser-scale social

responses to biophysical change are understood. This

approach is therefore applicable to coarse-scale systems

where autonomous subsystems of real-time social-ecolog-

ical interactions are identifiable.

In complex systems such as SESs, selection of indicators

involves a range of technical, biological, economic, social,

and political dimensions (McCool and Stankey 2004). In

addition, development and use of indicators are subject to

many personally negotiated decisions, normative and sub-

jective judgments, and assumptions, as well as disciplinary

and method-specific guidelines (United Nations 2002). It is

therefore necessary that the bases of, and rationale for,

indicator selection be explicit and open to scrutiny. In this

light, it is necessary that assumptions, strengths, and

weaknesses of both indicator selection and development

processes be made explicit and open to careful examina-

tion. The approaches used in this study meet these needs in

three significant ways. First, the theoretical model adopted

is rigorously developed, adequately integrative, rooted in

empirical reality, and communicated with metaphor and

example (following Holling 2001). The models explicitly

relate SER and its primary and secondary processes. Sec-

ond, the significant participation of the subjects in

generating place-specific measures for each indicator

minimizes ad hoc selection or selection based on expert

knowledge. As Wilkins (2003) argues, discursive interac-

tions (qualitative inquiry) with participants enable mutual

learning about social-ecological system processes. Third,

the empirical measurement and analytical methods used

have been and continue to be extensively debated,

improved on, and applied in several fields of empirical

enquiry (DeVellis 2003; Pugesek and others 2003; Shipley

2002). These methods minimized professional judgment in

the choice of retained items and aggregation of indicators

through empirical approaches such as internal consistency

and social desirability analyses and latent variable model

fitting.

The search for sustainability through management par-

adigms that emphasize human dominance in shaping

ecosystem dynamics implies scrutiny of human behavior if

we are to achieve sustainability (Norton 2003). It presents a

methodological advantage to the social scientist by situat-

ing humans as the major subjects of observation and

intervention. By design, the methods used in this study

maximize this advantage by relying on the ability of

humans to observe and self-report both ecological and

behavioral attributes of their interactions with nature.

Psychometric scales are widely used, through question-

naires, to assess and inform decision making on various

human-environment phenomena (e.g., Kneeshaw and oth-

ers 2004; Villacorta and others 2003). One drawback with

the use of scales in assessments of human-environment

interactions is the implied universality of the scales

developed vis-a-vis the obvious dynamism and context

specificity of human-environment interactions. Although

universality of scales expedites inquiry and facilitates

comparisons, it might be misleading to our understanding

and management of natural resource systems. Uses of

universal scales augment the chances of inadequately

reflecting place-specific realities and, thus, inform decision

making. The need to expedite observations, analyses, and

decision making should be balanced with that of missing

out on place-specific realities that could significantly

contribute to our understanding of social-ecological inter-

actions. That balance was achieved in this study via the

explicit involvement of the farmers in determining the

place-specific realities of farmer-farmland interactions and

the design of psychometric instruments measuring these

site-specific processes.

Conclusion

Human-environment interactive processes of interest to

managers and policy makers are complex and elusive; they

require simplification to enable communication to the

varied stakeholders, common in SESs. The idea of con-

structs in psychometric theory is used to measure elusive

Environmental Management (2008) 42:1077–1090 1087

123

human-environmental processes. System indicator theory

(Bossel 1999; Zhen and Routray 2003) is applied to

LVSEM thereby enabling simplification of complex

human-environmental processes into interpretable regres-

sion coefficients. As pointed by Wiren-Lehr (2001), this

approach allows us to close the gap between theory and

practice in social-ecological system monitoring and

assessment. The use of indicators in LVSEM permits

empirical understanding of unmeasured human-environ-

mental processes. The merger of these methods enabled

empirical determination of the state of SESs regarding

parameters such as resilience without the need to for direct

measurement of these parameters. Determination of system

state furnishes practitioners with the ability to reliably

recognize presence/absence of social-ecological resilience

and processes through which resilience is manifested.

Similarly, it allows for the reliable determination of the

state of system processes such as those employed to deal

with change and uncertainty. Simplified determination of

the state of system processes guides management and

policy interventions; repeated measurements enable

detection of trends and effects of interventions. This

approach therefore serves the dual purposes of monitoring

and assessment of human-environment systems as well as

providing a working definition of the state of SESs (fol-

lowing Yohe and Tol 2002).

The good fit of all CFA models for all primary processes

and that for system resilience (Table 4) further confirm the

empirical grounding of Folke and others’ (2003) model to

social-ecological realities of the Logone floodplain and

similar areas. Although SER cannot be judged in absolute

terms, because of its dynamic and metaphoric nature,

relationships with primary processes and, thus, the relative

degree to which these processes influence the resilience of

the system can be teased out and interpreted. Similarly, the

influence of secondary processes on primary processes can

be analyzed to enhance our understanding of the site-spe-

cific realities of these processes. This study demonstrates

how theoretical models, psychometrics, and LVSEM are

uniquely combined to determine the state of human-envi-

ronment systems in ways that are useful for management

and policy interventions. Combined with qualitative

inquiry (observations of field practices and interviews),

adequate interpretation of empirical findings is enabled and

demonstrates the necessity of integrated approaches for

effective monitoring and assessment of complex human-

environment interactions. Like most models (Moran 2000),

the five CFA models are simplifications of the complexity

and multidimensionality inherent in social-agricultural

interactions within the study area. However, according to

Grimm (1999), a model aims less at completeness than at

advancing understanding of the whole. This is exemplified

by the models’ utility in orienting the focus of detailed

qualitative analysis and intervention to enhance the ability

of farmers to cope with, adapt to, and shape change in ways

that do not compromise future options for adaptability. The

move from theory to LVSEM substantiates the empirical

grounding of Folke and others’ (2003) model. This shift

from specific and detailed understanding of qualitative data

to grounded generalism makes this approach useful in

understating and managing varied configurations of

human-environment interactions.

Acknowledgments I wish to recognize the Cameroon Association

for Environmental Education, specifically Mr. Aboukar Mohamat, for

assistance with field research and logistical support. I thank David

Bengston and Andy Holdsworth for comments on early drafts of this

paper. I would also like to thank two anonymous reviewers for their

suggested improvements to this paper. Financial support for this

research was provided by the MacArthur and Compton Foundations

through the Interdisciplinary Center for the Study of Global Change

and by the Graduate School of the University of Minnesota through

the Doctoral Dissertation Fellowship Program.

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