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Enhancing Resilience in Social-Ecological Systems: A Quantifiable Framework for
Adapting to Change
Meha Jain
Abstract
A majority of the literature discussing human adaptation to climate change in social-
ecological systems has not been quantitative in nature; discussions of adaptation are typically
based on theory or anecdotal case studies. It is, however, important to analytically identify which
factors lead to successful adaptation to climate change, in order to better determine how
communities can cope with climate shocks. This paper reviews the most highly cited studies that
empirically identify the drivers of adaptation to climate change, from the fields of human
ecology, anthropology, psychology, and economics. The primary factors that are cited are 1)
strong institutions and networks, 2) social memory and previous exposure to disturbance, 3)
access to capital, 4) cognitive factors, such as perceived risk and ability to adapt, and 5)
diversification of livelihoods. While these studies offer insights into the possible drivers of
adaptation, there are several ways in which future studies should be improved: new studies
should consider 1) biophysical factors that may constrain communities’ ability to adapt, 2)
multiple factors within the same multivariate analysis, and 3) the spatial and temporal scale at
which these factors may influence adaptation. Based on these considerations, a new analytical
framework for identifying the drivers of successful adaptation is outlined.
Resilience in Social-Ecological Systems
Social-ecological systems, or systems where ecosystems and humans are inextricably
linked, are facing unpredictable pressures and shocks due to global change and unsustainable
human use of resources (Chapin et al., 2010). These shocks may be internal to the system, such
as overuse of a particular natural resource, or external, such as possible impacts of climate
change. It is difficult to predict the effects of these shocks on social-ecological systems, given
that there are often thresholds and non-linearities in the system’s response to disturbance (Liu et
al, 2007; Burkett et al, 2005). This inability to predict and respond to future shocks is
problematic since it may result in the irreparable loss of ecosystem functions and services, and a
subsequent collapse of dependent human livelihoods (Olson, 2003). Scholars and policy makers
have called to make social-ecological systems more resilient to change (Smit and Pilifosova,
2001; Box 1). This will enhance the system’s capacity to respond to a wide range of shocks, and
ensure that the fundamental ecological and societal functions of the system are not compromised.
Although building resilience appears to be a possible strategy to cope with shocks, a clear
analytical framework to quantify which factors increase the resilience of a system does not
currently exist. Without knowing which system variables to measure or ensure are resilient to
shocks, it is almost impossible to determine how to best manage a social-ecological system for
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resilience. A new quantifiable framework, therefore, should be created that is based on the
empirical work that has been done to date on social-ecological resilience. This framework should
be inter-disciplinary, given that building resilience in social-ecological systems is an inter-
disciplinary problem (Liu et al, 2007). For example, studies have suggested that the stability of
small-holder agricultural systems to climate variability depends on 1) biophysical (i.e. soil and
climate), 2) social (i.e. community institutions), 3) economic (i.e. market prices for crops), and 4)
ecological (i.e. pest control) factors (Morton, 2007; Howden et al., 2007). Studying resilience in
these complex coupled human and natural systems requires a new sustainability science that is
inherently cross-disciplinary and team-based (Folke et al, 2002).
To help elucidate a possible analytical framework, this paper reviews studies that have
examined resilience to climate change in social-ecological systems from the disciplines of human
ecology, anthropology, economics, and psychology. The review specifically focuses on climate
change given that previous empirical resilience literature is predominately focused on climate
change. It is also important to understand resilience to climate change because climate shocks are
predicted to affect many communities worldwide. This paper will identify which factors may
increase communities’ resilience to climate change. Doing this will help determine which factors
should be considered in future inter-disciplinary studies of resilience, and possible ways to
quantify and consider these variables within a broader framework. This is necessary given that
few if any reviews have simultaneously considered the different disciplinary works on resilience.
It is also timely to create a new analytical framework given that many social-ecological systems
are being used unsustainably and are threatened by new, unpredictable shocks such as climate
change.
Linking Resilience, Adaptive Capacity, and Vulnerability
Although there is a growing body of literature examining the factors that contribute to
resilience in social-ecological systems, many of these studies use different terminology to
describe similar processes. The terms resilience, adaptation, maladaptation, adaptive capacity,
and vulnerability (Box 1) are often used interchangeably, since a universally-accepted
framework for defining these terms and their relationships to one another does not exist
(Gallopin, 2006). Figure 1 describes how these terms are related within a social-ecological
framework.
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Consider an agricultural system. Climate variables, such as precipitation, vary from year
to year. Precipitation impacts human livelihoods, since agricultural production is tied to the
amount of water available in a system. If a farmer is entirely dependent on rainfall for his crop
production, he may have high income and yields during ideal precipitation years but low income
and yields when the precipitation is too high (i.e. floods) or too low (i.e. droughts). This farmer is
said to be vulnerable to changes in climate, since his livelihood is very dependent on the
variability in climate. However, a farmer could become less vulnerable to climate by adapting
his livelihood strategies; he could adapt by switching to less climate-dependent livelihoods such
as salaried professions, gaining access to irrigation, or altering cropping strategies to suit current
climate patterns. Adaptation ensures that the farmer maximizes his income despite the variability
in climate. This farmer, whose income is not as heavily dependent on climate, is said to be
resilient to climate change. On the other hand, a farmer may also undergo maladaptation if he
alters his livelihood strategies in a way that make him less resilient to climate change. Certain
farmers are better able to adapt to climate change than others. For instance, a wealthy farmer
who can afford irrigation is better able to adapt to climate change than a poor rain-fed farmer.
This wealthy farmer who has an increased ability to adapt is defined as having increased
adaptive capacity.
Considering this framework, adaptive capacity is seen as one of the primary factors that
promotes the resilience of a system: a system with higher adaptive capacity will be more resilient
to disturbance (Nelson et al., 2007). On the other hand, systems are considered to be vulnerable
if they have low resilience and are greatly impacted by variable climates (Smit and Wandel,
2006). By reviewing studies that identify which factors enhance resilience or decrease
vulnerability, I will modify Figure 1 to create an analytical framework for identifying these
factors and their relative importance.
Factors Facilitating Resilience
I reviewed the most cited literature on adaptation, adaptive capacity, and vulnerability to
climate change to identify common drivers that are predicted to enhance adaptive capacity and
limit vulnerability. Specifically, I conducted three different searches in the ISI Web of
Knowledge database using the terms “human adaptation climate change”, “human vulnerability
climate change”, and “adaptive capacity climate change” respectively. Each of these searches
returned between 300 to 500 journal articles. I then read through each article’s title and abstract
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to determine whether the paper actually empirically identified factors associated with adaptation
and resilience using either case studies or regional-scale analyses. In addition, this review only
considers papers that were cited ten or more times to control for quality. In sum, over one-
hundred papers that match the above criteria are considered in this review.
To determine what factors may be important for adaptation and resilience, I read through
each paper and identified what drivers were considered to be important for resilience based on
observations in case studies or statistical analyses in multivariate studies. I considered a factor to
be highly cited if it was mentioned in more than five studies. Each of the most cited factors are
described in detail below.
Institutions
One of the most highly cited factors that enhances adaptive capacity is the presence of
local institutions. It is argued that institutions can aid adaptive capacity in two ways: 1)
institutions result in the sustainable use of natural resources, which makes systems more resilient
to climate change; and 2) institutions can increase adaptive capacity by creating both horizontal
and vertical networks (Adger, 2003; Tompkins and Adger, 2004; Folke et al., 2005).
First, many case studies have suggested that certain institutions improve the sustainability
of resource use in community-managed landscapes (Ostrom et al., 1999, Nagendra, 2007, Adger
and Vincent, 2007). Scholars have argued that systems that conserve natural resources and their
supporting ecosystem processes are more resilient to perturbations than degraded ecosystems
with no sustainable use policies (Adger, 2003; Tompkins and Adger, 2004; Folke et al., 2004).
However, the empirical evidence showing this benefit to adaptive capacity is currently weak and
must be better established. This is because these previous studies have not quantified resilience
of the system, and how resilience changes with the presence of institutions.
Second, horizontal networks within and between institutions are thought to enhance
adaptive capacity (Armitage, 2005). Horizontal networks are relationships between people and
groups at the same political scale (e.g. between individuals or village governing bodies). Local
institutions encourage communication among decision-makers, which allow them to adapt
management strategies and be more flexible during times of uncertainty (Berkes et al., 2003;
Ford et al., 2006). Horizontal networks, such as inter-community trade of resources, have been
found to increase resilience to climate variability; inter-community trade between Inuit
communities in Arctic Canada ensured that if one community’s resources were negatively
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impacted by climate shocks, necessary resources could still be obtained through trade (Berkes
and Jolly, 2001). Institutions that form vertical networks have also been shown to increase the
adaptive capacity of a system (Adger et al., 2005; Folke et al., 2005). Unlike horizontal
networks, vertical networks form relationships between actors at different political scales (e.g.
between a village governing body and national political leaders). Specifically, case studies in
Trinidad and Tabago suggest that vertical networks between state and government officials as
well as local institutions help with disaster planning for hurricanes. These studies argue that, if a
hurricane were to occur, the strong relationship between the local institution and those in charge
of disaster planning would reduce the negative impacts of the hurricane on the community
(Tompkins and Adger, 2004).
Previous Exposure to Climate Variability and Social Memory
Several economic as well as anthropological case studies have suggested that exposure to
previous climate variability results in communities that are better able to adapt to future change.
Using a Ricardian approach (Box 2), Polsky and Easterling (2001) suggest that districts in the
United States that were historically exposed to climate variability better mitigated the negative
effects of climate shocks on crop yields. Ricardian analyses do not quantify adaptation
specifically, instead they use impact assessments on land value as a proxy for adaptation
(Smithers and Smit, 1997); in this case, land values were used as a proxy for agricultural
adaptation (Mendelsohn et al., 1994). While impressive in spatial scale, these broad-scale
analyses are unable to determine the mechanisms behind why exposure to variability enhances
adaptive capacity. One way to identify mechanisms would be to conduct finer scale mechanistic
case studies within the context of these broad regional patterns (Figure 2).
Several anthropological studies have also stated that exposure to previous climate
variability results in increased adaptive capacity to future change. Case studies qualitatively
suggest possible mechanisms for why exposure to previous variability enhances adaptive
capacity. Experience with previous climate shocks and subsequent effects may result in
increased social memory that better allows communities to adapt to future shocks. For example,
fishing communities in Southeast Asia who had experienced frequent tsunamis in the past were
better able to prepare for and survive the large tsunami that hit in 2004. This is because the
community had social memory in the form of inherited local knowledge of tsunamis as well as
institutional memory allowing for better preparedness (Folke et al., 2005). In addition, farmers in
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Southern Canada are more likely to adapt their cropping strategies if they have experienced
drought conditions multiple times over the past several years (Smit et al, 1996). These case
studies suggest that previous variability in climate leads to social memory of adaptation
strategies and their benefits.
Access to Capital and Development
Small-scale case studies and broad-scale analyses examining vulnerability suggest that
access to capital and increased development result in enhanced adaptive capacity (Ziervogel and
Bharwani, 2006; Brooks et al, 2007). Access to capital gives individuals more options to adapt
their use of natural resources in a way that reduces the impacts of climate shocks on their
livelihoods.
Small-scale studies examining the impacts of climate variability on small-holder
agricultural communities in South Africa suggest that poorer farmers have less access to inputs
and opportunities that may ameliorate negative impacts of climate variability (Ziervogel and
Bharwani, 2006). Poorer farmers cannot afford fertilizer or irrigation inputs, making them more
susceptible to present ecological conditions and climate fluctuations. Furthermore, poorer
farmers have less access to other forms of livelihoods that are not dependent on climate
variability, such as business opportunities outside of the agricultural sector.
In addition, broad-scale, national-level studies that consider how a variety of socio-
economic and biophysical factors influence vulnerability suggest that the development level of a
community, measured by increased access to healthcare, higher literacy rates, and increased per
capita income, is a good predictor variable. Specifically, these studies use nation-wide
mortalities and displacements caused after a climatic disturbance, such as a flood or a hurricane,
as a measure of vulnerability. Brooks et al (2007) found that health and education indicators,
which are associated with development, are the best predictors of total deaths after a disaster.
Income has also been shown to be a positive indicator of resilience in communities susceptible to
displacement after flooding (Yohe and Tol, 2002). Therefore, these studies suggest that regional
to global studies of adaptive capacity should consider the overall health, educational, and
economic development of a community as factors in their analyses.
Cognitive Factors
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While there are not enough case studies to draw broad-scale conclusions, several
psychology and behavioral economics studies have identified cognitive factors that are
associated with enhanced adaptive capacity. An individual’s risk perception of the likelihood of a
disturbance event has been shown to affect one’s decision to adapt to a disturbance. Perceived
adaptive capacity, coupled with risk perception, also has been shown to play a strong role in
whether an individual adapts to climate change. For example, even if someone believes a
disturbance may occur (i.e. risk perception), he still may not adapt because he believes that there
is nothing he can do to reduce the negative impacts of the disturbance. These perceptions of risk
and adaptive capacity have been found to be positively correlated with decisions to adapt; socio-
cognitive models considering perceived risk and adaptive capacity have explained up to forty
percent of the variance in people’s decisions to adapt to a disturbance (Grothmann and Patt,
2005).
Finally, agent-based models have also been used to determine what factors result in
decisions to adapt to disturbance (Box 2). Specifically, agent-based models have been used to
determine the cause and effect of farmers’ responses to climate variability in Limpopo district,
South Africa (Bharwani et al., 2005). Studies have found that whether a farmer adapts his
cropping strategies based on climate variability depends on the amount of trust that he has in
local climate forecasts. This once again emphasizes the role of cognitive measures in adaptation;
it is possible that accurate climate forecasts will go unused because farmers believe that they are
inaccurate and untrustworthy.
Diversification
Several anecdotal case studies have also suggested that communities enhance their
adaptive capacity by diversifying their livelihood strategies (Howden et al, 2007). This may take
place in two different ways. A community may diversify their primary source of livelihood, by
increasing the range of their livelihood practices (Ziervogel and Bharwani, 2006). For example,
traditional hunting communities in the Arctic diversify the type and amount of animals they hunt
to minimize risk and uncertainty (Berkes and Jolly, 2001). Similarly, farmers in Indian
communities often plant a variety of crops, ranging from those that do well with large amounts
of rain to more drought tolerant crops; this increases the chance of at least one crop succeeding
under unpredictable climate (Saxena et al., 2005). On the other hand, a community may more
broadly diversify their livelihoods by practicing at least two or more livelihoods. For example,
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due to increasing climate variability, pastoralists in Kenya are starting to shift towards mixed
agro-pastoral systems (Little, 2000). Added income from agriculture helps ameliorate the
possible loss of income due to climate impacts on pastoralism. On the other hand, agricultural
communities are shifting towards pastoralism in South Africa because they believe agricultural
yields are much more tied to climate shocks than livestock (Thomas et al., 2007). Therefore, by
diversifying their livelihood portfolio, agro-pastoralists minimize risk associated with climate
variability.
Future Directions for Resilience Studies
Although there is a growing body of empirical literature examining the factors that
enhance adaptive capacity, there are several improvements that future studies should make.
Studies should consider how biophysical variables may facilitate or constrain adaptive capacity.
Future studies should also consider multiple drivers of adaptive capacity within the same
analysis. It is also important to consider both the spatial and temporal scale of the factors
identified to enhance adaptive capacity. Based on these considerations, a new empirical
framework for examining resilience to climate change in social-ecological systems is postulated.
Biophysical Variables
To date, most empirical work that examines resilience of social-ecological systems has
overlooked possible biophysical drivers. While scholars may overlook biophysical factors
because they are hesitant to argue for environmental determinism of social processes, it has been
shown that biophysical factors can affect and constrain social processes. Considering biophysical
drivers is necessary given that several factors have been theorized to play a role in constraining
adaptive capacity specifically (Gajbhiye and Mandal, 2009). Soil type may constrain a farmer’s
ability to adapt to climate variability (Luers, 2005); poor soils may not allow particular crops to
thrive, even if they are the ideal crop for a given climate.
Biophysical factors have also been shown to play an important role in influencing the
effectiveness of social institutions. Tucker et al (2007) found that soil nitrogen content, annual
rainfall, and annual temperature were all positively correlated with improved forest conditions,
and these improved forest conditions resulted in stronger institutions. The authors argue that
better forest quality give institutions more incentive to protect forest cover, which make their
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institutions stronger and more effective. While this study was not analyzing adaptive capacity
specifically, it suggests that biophysical factors can influence and constrain social processes.
Multi-factor Analyses
Despite the wide variety of factors thought to contribute to adaptive capacity, few studies
have considered multiple factors within the same analysis. Doing so is important to understand
the relative importance and strength of each factor on adaptive capacity. As noted in the section
above, studies have found that cognitive factors of risk as well as exposure to previous
disturbance both enhance adaptive capacity. It is, however, possible that these two factors are
highly correlated; communities that have been historically exposed to disturbance may be better
able to adapt because they have both increased risk perception as well as perceived adaptive
capacity. Exposure to previous disturbance may make it seem more likely that an additional
disturbance will take place (i.e. increased perceived risk) and it may also provide experience with
adapting to a disturbance (i.e. increased perceived adaptive capacity). Therefore, only
considering one factor without the other may exaggerate the real influence of each factor on
adaptive capacity.
It is also important to consider what other factors may be driving people’s actions other
than climate variables. Many studies often attribute changes in agricultural cropping strategies to
changes in climate, when in reality they may simultaneously occur due to a variety of other
factors, including changes in crop prices (Smit et al., 1996). Variability in cropping decisions and
yields has also been attributed to labor supply as well as groundwater quality (Gregory et al.,
2005). Therefore, future studies should collect data on possible non-climate related drivers that
are thought to also influence people’s behavior.
Issues of Scale
The spatial and temporal scale at which each hypothesized driver contributes to adaptive
capacity and resilience must also be examined (Turner et al., 2003; Vincent, 2007). Considering
spatial scale, cognitive factors may act on the individual whereas institutional factors may
influence the community or region more broadly. Considering temporal scale, an individual’s
perceived risk may influence adaptive capacity on a relatively short-term basis; however, longer-
scale processes such as biophysical processes will influence a community’s adaptive capacity on
relatively longer time-scales (Allison and Hobbs, 2004). Studies have also suggested that current
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adaptations may be adequate for short-term climate shocks, but are inadequate to ameliorate
long-term climate change. Canadian farmers are currently resilient to climate shocks due to
successful adaptation strategies and technologies (e.g. irrigation), however it is unclear how
these communities will cope with future increased unpredictability in climate (Bryant et al.,
2000). It is clearly important to account for the spatial and temporal scale at which each factor
may have influence on adaptive capacity.
Considering the spatial and temporal limitations of each driver suggests that a
hierarchical study design is needed to understand the drivers of adaptive capacity at different
scales. For instance, broad-scale Ricardian analyses can be conducted to understand which
social, biophysical, and economic factors lead to increased adaptive capacity at the regional-
scale. Smaller nested case studies should then be conducted that examine the effects of finer
scale drivers, such as individual cognitive processes, on local adaptive capacity. While doing this
may not allow different-scale processes to be compared with one another, it will allow for a
detailed understanding of finer-scale processes within the context of broader phenomena.
New Framework
Given the considerations outlined above, a new framework should 1) consider methods to
measure the possible inter-disciplinary drivers currently thought to enhance adaptive capacity, 2)
include biophysical variables, which are often overlooked in current empirical resilience studies,
3) consider the spatial and temporal scales of each factor’s influence on adaptive capacity, and 4)
conduct nested studies to understand finer-scale processes within the context of broader
phenomena. Figure 2 outlines one possible framework.
This new framework provides a more comprehensive method to indentify which factors
contribute to adaptive capacity in social-ecological systems. Applying this framework to
agricultural adaptation to climate shocks, analyses should be conducted at both regional and
individual farmer decision-making scales. Considering regional scale processes, networks with
other farming communities may increase a community’s adaptive capacity through trade; during
a dry year, farmers may obtain more drought-resistant seeds from other neighboring
communities. Soil fertility at a regional-scale may also affect adaptive capacity since
communities with higher soil fertility may obtain higher yields even during years with extreme
climate events. A more developed community may have higher adaptive capacity due to
increased infrastructure that helps communities cope with climate shocks, such as wells to
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increase access to irrigation. The importance of these regional-scale processes on the resilience
of a community to climate change should be assessed within the same multivariate analysis (e.g.
Ricardian analysis).
After conducting these broader scale analyses, it is important to conduct nested case
studies to determine how these regional processes affect farmers at the individual level. For
example, previous exposure to regional-scale climate variability may influence how an
individual farmer perceives risk and adaptive capacity. The amount of livelihood options
available at the regional scale will also influence the ability of a farmer to adapt his livelihood
strategies at the local level; if a village is not connected to the market, a farmer’s options to
diversify will be limited to local village professions. The regional networks that a village has
with other nearby villages will also influence the decision-making process of an individual
farmer; no matter how well networked a farmer is, he will not have access to new seeds from
other villages if no social or economic networks link his village with other villages. By carefully
selecting case studies within a broader regional analysis, scholars can better determine how
regional processes affect individuals at the local scale.
Case studies should also be selected to highlight possible mechanisms for regional-scale
patterns. For example, if a Ricardian model suggests that social networks are important for
successful adaptation, it is important to select case studies that represent areas with good social
networks and areas with weak social networks to determine how the presence of strong social
networks influences adaptation. Although it will be difficult to select enough villages to gain a
statistical understanding of why social networks are important for adaptive capacity at the
regional level, case studies can offer important qualitative insights into possible mechanisms.
These examples highlight the importance of studying both broad-scale processes and
finer-scale decision-making within the same experimental design. One proposed method to do
this is to use a regional-scale model (e.g. Ricardian model) to determine which variables impact
resilience and their relative strength at the regional level. Researchers should then select case
study regions based on the results of this broad-scale analysis to determine how regional
processes affect decisions at the individual level and to also identify possible mechanisms for the
factor’s importance. Finally, scholars should also use individual-level models (e.g. agent-based
model) to assess what factors influence decision-making and its effects on resilience at the
individual level. This hierarchical, nested approach will elucidate which factors are important for
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resilience at a regional level, possible mechanisms for why these regional-scale factors are
important, and which factors are important for resilience at the individual decision-making level.
Conclusions
Given that social-ecological systems are facing unpredictable pressures and disturbances
due to global change and unsustainable use of resources, it is important to understand what
factors may make these systems more resilient to climate change. By doing so, we may be able
to manage systems to cope with a wide range of climate shocks, ensuring that the fundamental
ecological and societal functions of the system are not compromised.
There is a growing body of literature that empirically determines which factors enhance
the adaptive capacity and resilience of social-ecological systems. Here I review these empirical
studies to determine which factors are most cited as drivers of adaptive capacity. In summary, 1)
strong institutions and networks, 2) social memory and previous exposure to disturbance, 3)
access to capital, 4) cognitive factors, such as perceived risk and adaptive capacity, and 5)
diversification of livelihoods are thought to influence adaptive capacity. Current studies are
limited because they 1) neglect biophysical drivers that may constrain adaptive capacity, 2) do
not consider multiple drivers within the same analysis, and 3) often ignore the spatial and
temporal scale of each driver’s effect on adaptive capacity. Based on these considerations, a new
framework is postulated that considers multiple socio-economic and biophysical drivers of
adaptive capacity at local and regional scales. This hierarchical framework should be used to
design new, empirical studies that examine which factors are associated with increased adaptive
capacity; doing so will help determine how social-ecological systems can be made more resilient
to unpredictable climate shocks and future change.
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Boxes and Figures
Figure 1. This figure depicts a social-ecological system, where human use of ecosystems results in livelihood
benefits. Individuals or communities are said to be more vulnerable to climate if their livelihoods are directly
dependent on climate patterns. For instance, vulnerable individual’s income fluctuates with changes in precipitation.
Individuals or communities are more resilient to climate if their livelihoods are less dependent on climate patterns.
For instance, individuals may adapt their livelihood strategies in a way where they obtain high incomes even during
extreme climate years (e.g. droughts and floods). The higher the individual or community’s income is across climate
variable years, the more resilient it is to climate change. An individual’s ability to adapt to climate change is defined
as adaptive capacity.
Box 1. Definitions of Vulnerability, Resilience, Adaptation, Maladaptation, and Adaptive Capacity
Related to Climate Change
Vulnerability – Outcome of interest is extremely influenced by climate.
Resilience – Outcome if interest is not influenced by climate. Resilience is increased when the outcome of
interest is maximized across climate variable years.
Adaptation – Alteration of livelihood or management strategies to become more resilient to climate.
Maladaptation – Alteration of livelihood or management strategies that leads to decreased resilience to climate.
Adaptive Capacity – The ability of an individual or communities to adapt to climate change.
Box 2. Analytical Methods to Study Factors that Facilitate Adaptive Capacity and Resilience
Ricardian Models – Land values are used as a proxy for agricultural adaptation. If land values remain high even
during times of unfavorable climates, this suggests that agricultural production is adapted to be resilient to
climate shocks
Agent-based Models – Models that simulate the actions of individuals to determine their effects on the system
as a whole
Socio-cognitive Models – Models that consider the social and cognitive decision-making of individuals to
determine their actions within a system
Vulnerability Studies – These studies typically use multivariate statistics to determine which socio-economic
and biophysical factors lead to the least loss in life or infrastructure after a disturbance at a regional scale
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Figure 2. This figure builds on the original framework for analyzing resilience and adaptive capacity outlined in
Figure 1. Both individual and regional-scale factors that may contribute to adaptive capacity are outlined. The figure
suggests that a hierarchical approach should be taken to understand the drivers of adaptive capacity at regional
scales and finer individual decision-making at local scales. Finer scale analyses should be understood within the
broader regional context.
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Bibliography Adger, W. (2003). Social capital, collective action, and adaptation to climate change. Economic Geography , 79 (4),
387-404.
Adger, W., & Vincent, K. (2005). Uncertainty in adaptive capacity. Comptes Rendus Geosciences , 337 (4), 399-
410.
Adger, W., Hughes, T., Folke, C., Carpenter, S., & Rockstrom, J. (2005). Social-ecological resilience to coastal
disasters. Science , 309 (5737), 1036.
Allison, H., & Hobbs, R. (2004). Resilience, adaptive capacity, and the" Lock-in Trap" of the Western Australian
agricultural region. Ecology And Society , 9 (1), 3.
Armitage, D. (2005). Adaptive capacity and community-based natural resource management. Environmental
Management , 35 (6), 703-715.
Berkes, F., & Jolly, D. (2002). Adapting to climate change: social-ecological resilience in a Canadian western Arctic
community. Conservation Ecology , 5 (2), 18.
Bharwani, S., Bithell, M., Downing, T., New, M., Washington, R., & Ziervogel, G. (2005). Multi-agent modelling
of climate outlooks and food security on a community garden scheme in Limpopo, South Africa. Philosophical
Transactions of the Royal Society B: Biological Sciences , 360 (1463), 2183.
Brooks, N., Neil Adger, W., & Mick Kelly, P. (2005). The determinants of vulnerability and adaptive capacity at the
national level and the implications for adaptation. Global Environmental Change Part A , 15 (2), 151-163.
Bryant, C., Smit, B., Brklachic, M., Johnston, T., Smithers, J., Chiotti, Q., & Singh, B (2000). Adaptation in
Canadian Agriculture to Climatic Variability and Change. Climatic Change 45. 181-201.
Burkett, V., Wilcox, D., Stottlemyer, R., Barrow, W., Fagre, D., Baron, J., et al. (2005). Nonlinear dynamics in
ecosystem response to climatic change: case studies and policy implications. Ecological Complexity , 2 (4), 357-394.
Folke, C., Carpenter, S., Elmqvist, T., Gunderson, L., Holling, C., & Walker, B. (2002). Resilience and sustainable
development: building adaptive capacity in a world of transformations. Journal Information , 31 (5).
Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L., et al. (2004). Regime shifts,
resilience, and biodiversity in ecosystem management.
Folke, C., Hahn, T., Olsson, P., & Norberg, J. (2005). Adaptive governance of social-ecological systems. Annual
Review Of Environment And Resources , 30 (1), 441.
Gajbhiye, K., & Mandal, C. (2009). Agro-ecological zones, their soil resource and cropping systems. Status of Farm
Mechanization in India, CED Documentation. Accessed November .
Ford, J., Smit, B., & Wandel, J (2006). Vulnerability to climate change in the Arctic: A case study from Arctic Bay,
Canada. Global Environmental Change 16; 145-160.
Gallopín, G. (2006). Linkages between vulnerability, resilience, and adaptive capacity. Global Environmental
Change , 16 (3), 293-303.
Gregory, P., Ingram, J., Brklacich, M. (2005), Climate Change and Food Security. Philosophical Transactions:
Biological Sciences. 360 (1463); 2139 - 2148.
Grothmann, T., & Patt, A. (2005). Adaptive capacity and human cognition: the process of individual adaptation to
climate change. Global Environmental Change Part A , 15 (3), 199-213.
16
Howden, S., Soussana, J., Tubiello, F., Chhetri, N., Dunlop, M., & Meinke, H. (2007). Adapting agriculture to
climate change. Proceedings of the National Academy of Sciences , 104 (50), 19691.
Chapin, S., Carpenter, S., Kofinas, G., Folke, C., Abel, N., Clark, W., et al. (2010). Ecosystem stewardship:
sustainability strategies for a rapidly changing planet. Trends in Ecology & Evolution , 25 (4), 241-249.
Little, P., Smith, K., Cellarius, B., Coppock, D., & Barrett, C. (2001). Avoiding disaster: diversification and risk
management among East African herders. Development and Change , 32 (3), 401-433.
Liu, J., Dietz, T., Carpenter, S., Alberti, M., Folke, C., Moran, E., et al. (2007). Complexity of
coupled human and natural systems. Science , 317 (5844), 1513.
Luers, A. (2005). The surface of vulnerability: an analytical framework for examining environmental change.
Global Environmental Change Part A , 15 (3), 214-223.
Mendelsohn, R., Nordhaus, W., & Shaw, D. (1994). The impact of global warming on agriculture: a Ricardian
analysis. The American Economic Review , 84 (4), 753-771.
Morton, J. (2007). The impact of climate change on smallholder and subsistence agriculture. Proceedings of the
National Academy of Sciences , 104 (50), 19680.
Nagendra, H. (2007). Drivers of reforestation in human-dominated forests. Proceedings of the National Academy of
Sciences , 104 (39), 15218.
Nelson, D., Adger, W., & Brown, K. (2007). Adaptation to environmental change: contributions of a resilience
framework.
Olsson, P. (2003). Building capacity for resilience in social-ecological systems. Ph.D Dissertation. Stolkhom
University.
Ostrom, E., Burger, J., Field, C., Norgaard, R., & Policansky, D. (1999). Revisiting the commons: local lessons,
global challenges. Science , 284 (5412), 278.
Polsky, C., & Easterling, W. (2001). Adaptation to climate variability and change in the US Great Plains: A multi-
scale analysis of Ricardian climate sensitivities. Agriculture .
Saxena, K., Maikhuri, R., & Rao, K. (2005). Changes in agricultural biodiversity: implications for sustainable
livelihood in the Himalaya. Journal of Mountain Science , 2 (1), 23-31.
Smit, B., McNabb, D., & Smithers, J (1996). Agricultural Adaptation to Climatic Variation. Climatic Change, 33. 7-
29.
Smit, B., & Pilifosova, O. (2003). Adaptation to climate change in the context of sustainable development and
equity. Sustainable Development , 8 (9), 9–28.
Smit, B., & Wandel, J. (2006). Adaptation, adaptive capacity and vulnerability. Global
Environmental Change , 16 (3), 282-292.
Smithers, J., & Smit, B. (1997). Human adaptation to climatic variability and change. Global Environmental Change
, 7 (2), 129-146.
Tompkins, E., & Adger, W. (2004). Does adaptive management of natural resources enhance resilience to climate
change? Ecology And Society , 9 (2), 10.
Thomas, D., Twyman, C., Osbahr, H., Hewitson, B (2007). Adaptation to climate change and variability: farmer
responses to intra-seasonal precipitation trends in South Africa. Climatic Change 83; 301-322.
17
Tucker, C., Randolph, J., & Castellanos, E. (2007). Institutions, biophysical factors and history: an integrative
analysis of private and common property forests in Guatemala and Honduras. Human Ecology , 35 (3), 259-274.
Turner, B., Kasperson, R., Matson, P., McCarthy, J., Corell, R., Christensen, L, Eckley, N., Kasperson, J., Luers, A.,
Martello, M., Polsky, C., Pulsipher, A, & Schiller, A (2003). A framework for vulnerability analysis in sustainability
science. Proceedings of the National Academy of Sciences, 100 (14), 8074-8079.
Vincent, K. (2007). Uncertainty in adaptive capacity and the importance of scale. Global Environmental Change ,
17 (1), 12-24.
Yohe, G., & Tol, R. (2002). Indicators for social and economic coping capacity--moving toward a working
definition of adaptive capacity. Global Environmental Change , 12 (1), 25-40.
Ziervogel, G., & Bharwani, S. (2006). Adapting to climate variability: pumpkins, people and policy. Natural
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