Multiple Scales Of Organization Research Articles

Multiscale analysis of Klf10's impact on the passive mechanical properties of murine skeletal muscle

Skeletal muscle is a hierarchical structure composed of multiple organizational scales. A major challenge in the biomechanical evaluation of muscle relates to the difficulty in evaluating the experimental mechanical properties at the different organizational levels of the same tissue. Indeed, the ability to integrate mechanical properties evaluated at various levels will allow for improved assessment of the entire tissue, leading to a better understanding of how changes at each level evolve over time and/or impact tissue function, especially in the case of muscle diseases.Therefore, the purpose of this study was to analyze a genetically engineered mouse model (Klf10 KO: Krüppel-Like Factor 10 knockout) with known skeletal muscle defects to compare the mechanical properties with wild-type (WT) controls at the three main muscle scales: the macroscopic (whole muscle), microscopic (fiber) and submicron (myofibril) levels. Passive mechanical tests (ramp, relaxation) were performed on two types of skeletal muscle (soleus and extensor digitorum longus (EDL)).Results of the present study revealed muscle-type specific behaviors in both genotypes only at the microscopic scale. Interestingly, loss of Klf10 expression resulted in increased passive properties in the soleus but decreased passive properties in the EDL compared to WT controls.At the submicron scale, no changes were observed between WT and Klf10 KO myofibrils for either muscle; these results demonstrate that the passive property differences observed at the microscopic scale (fiber) are not caused by sarcomere intrinsic alterations but instead must originate outside the sarcomeres, likely in the collagen-based extracellular matrix. The macroscopic scale revealed similar passive mechanical properties between WT and Klf10 KO hindlimb muscles.The present study has allowed for a better understanding of the role of Klf10 on the passive mechanical properties of skeletal muscle and has provided reference data to the literature which could be used by the community for muscle multiscale modeling.

Disequilibrium and complexity across scales: a patch-dynamics framework for organizational ecology

Based on equilibrium assumptions, traditional ecological models have been widely applied in the fields of management and organization studies. While research using these models is still ongoing, studies have nonetheless struggled with ways to address multiple levels of analysis, uncertainty, and complexity in their analyses. This paper conceptualizes the dynamic co-evolution mechanisms that operate in an ecosystem across multiple organizational scales. Specifically, informed by recent advances in modelling in biology, a general ‘patch-dynamics’ framework that is theoretically and methodologically able to capture disequilibrium, uncertainty, disturbances, and changes in organizational populations or ecosystems, as complex and dynamically evolving resource environments are introduced. Simulation models are built to show the patch-dynamics framework’s functioning and test its robustness. The patch-dynamics framework and modelling methodology integrates equilibrium and disequilibrium perspectives, co-evolutions across multiple organization levels, uncertainties, and random disturbances into a single framework, opening new avenues for future research on topics in the field of management and organization studies, as well as on the mechanisms that shape ecosystems. Such a framework has the potential to help analyse the sustainability and healthiness of the business environment, and deserves more attention in future research on management and organization theory, particularly in the context of significant uncertainty and disturbances in business and management practice. Overall, the paper offers a distinct theoretical perspective and methodology for modelling population and ecosystem dynamics across different scales.

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing.

Most applications of 3-dimensional (3D) printing for presurgical planning have been limited to bony structures and simple morphological descriptions of complex organs due to the fundamental limitations in accuracy, quality, and efficiency of the current modeling paradigm. This has largely ignored the soft tissue critical to most surgical specialties where the interior of an object matters and anatomical boundaries transition gradually. Therefore, the needs of the biomedical industry to replicate human tissue, which displays multiple scales of organization and varying material distributions, necessitate new forms of representation. Presented here is a novel technique to create 3D models directly from medical images, which are superior in spatial and contrast resolution to current 3D modeling methods and contain previously unachievable spatial fidelity and soft tissue differentiation. Also presented are empirical measurements of novel, additively manufactured composites that span the gamut of material stiffnesses seen in soft biological tissues from MRI and CT. These unique volumetric design and printing methods allow for deterministic and continuous adjustment of material stiffness and color. This capability enables an entirely new application of additive manufacturing to presurgical planning: mechanical realism. As a natural complement to existing models that provide appearance matching, these new models also allow medical professionals to "feel" the spatially varying material properties of a tissue simulant-a critical addition to a field in which tactile sensation plays a key role.

Potential Phenotyping Methodologies to Assess Inter- and Intravarietal Variability and to Select Grapevine Genotypes Tolerant to Abiotic Stress.

Plant phenotyping is an emerging science that combines multiple methodologies and protocols to measure plant traits (e.g., growth, morphology, architecture, function, and composition) at multiple scales of organization. Manual phenotyping remains as a major bottleneck to the advance of plant and crop breeding. Such constraint fostered the development of high throughput plant phenotyping (HTPP), which is largely based on imaging approaches and automatized data retrieval and processing. Field phenotyping still poses major challenges and the progress of HTPP for field conditions can be relevant to support selection and breeding of grapevine. The aim of this review is to discuss potential and current methods to improve field phenotyping of grapevine to support characterization of inter- and intravarietal diversity. Vitis vinifera has a large genetic diversity that needs characterization, and the availability of methods to support selection of plant material (polyclonal or clonal) able to withstand abiotic stress is paramount. Besides being time consuming, complex and expensive, field experiments are also affected by heterogeneous and uncontrolled climate and soil conditions, mostly due to the large areas of the trials and to the high number of traits to be observed in a number of individuals ranging from hundreds to thousands. Therefore, adequate field experimental design and data gathering methodologies are crucial to obtain reliable data. Some of the major challenges posed to grapevine selection programs for tolerance to water and heat stress are described herein. Useful traits for selection and related field phenotyping methodologies are described and their adequacy for large scale screening is discussed.

The Collective Behavior of Ant Groups Depends on Group Genotypic Composition.

Recently, researchers have documented variation between groups in collective behavior. However, how genetic variation within and between groups contributes to population-level variation for collective behavior remains unclear. Understanding how genetic variation of group members relates to group-level phenotypes is evolutionarily important because there is increasing evidence that group-level behavioral variation influences fitness and that the genetic architecture of group-level traits can affect the evolutionary dynamics of traits. Social insects are ideal for studying the complex relationship between individual and group-level variation because they exhibit behavioral variation at multiple scales of organization. To explore how the genetic composition of groups affects collective behavior, we constructed groups of pharaoh ants (Monomorium pharaonis) from 33 genetically distinct colonies of known pedigree. The groups consisted of either all workers from the same single colony or workers from two genetically different colonies, and we assayed the exploration and aggression of the groups. We found that collective exploration, but not aggression, depended on the specific genotypic combination of group members, i.e., we found evidence for genotype-by-genotype epistasis for exploration. Group collective behavior did not depend on the pedigree relatedness between genotypes within groups. Overall, this study highlights that specific combinations of genotypes influence group-level phenotypes, emphasizing the importance of considering nonadditive effects of genotypic interactions between group members.

Ontogenetic diversity buffers communities against consequences of species loss.

Biodiversity can be measured at multiple organizational scales. While traditional studies have focused at taxonomic diversity, recent studies have emphasized the ecological importance of diversity within populations. However, it is unclear how these different scales of diversity interact to determine the consequence of species loss. Here we asked how predator diversity and presence of ontogenetic diversity within predator populations influences community structure. Ontogenetic diversity arises from shifts in the traits and ecology of individuals during ontogeny and it is one of the biggest sources of intraspecific diversity. However, whether it dampens or strengthens the negative consequences of with species loss is poorly understood. To study the interaction of species diversity and ontogenetic diversity, we experimentally manipulated predator species diversity and diversity of developmental stages within focal predator species and analysed their joint effect on predator and prey survival, biomass and prey community structure in experimental pond systems. While individual effects of ontogenetic diversity were often species specific, losing predator species from the community often had a much smaller or no effect on prey survival, biomass or community structure when all predator populations had high ontogenetic diversity. Thus, ontogenetic diversity within populations buffered against some of the consequences of biodiversity loss at higher organizational levels. Because the experiment controlled mean per capita size and biomass across structured versus unstructured populations, this pattern was not driven by differences in biomass of predators. Instead, results suggest that effects were driven by changes in the functional roles and indirect interactions across and within species. This indicates that even if all environmental conditions are similar, differences in the intrinsic structure of populations can modify the consequences of biodiversity loss. Together, these results revealed the importance of ontogenetic diversity within species for strengthening the resilience of natural communities to consequences of biodiversity loss and emphasize the need to integrate biodiversity patterns across organizational scales.

Behavior-related gene regulatory networks: A new level of organization in the brain

Neuronal networks are the standard heuristic model today for describing brain activity associated with animal behavior. Recent studies have revealed an extensive role for a completely distinct layer of networked activities in the brain—the gene regulatory network (GRN)—that orchestrates expression levels of hundreds to thousands of genes in a behavior-related manner. We examine emerging insights into the relationships between these two types of networks and discuss their interplay in spatial as well as temporal dimensions, across multiple scales of organization. We discuss properties expected of behavior-related GRNs by drawing inspiration from the rich literature on GRNs related to animal development, comparing and contrasting these two broad classes of GRNs as they relate to their respective phenotypic manifestations. Developmental GRNs also represent a third layer of network biology, playing out over a third timescale, which is believed to play a crucial mediatory role between neuronal networks and behavioral GRNs. We end with a special emphasis on social behavior, discuss whether unique GRN organization and cis-regulatory architecture underlies this special class of behavior, and review literature that suggests an affirmative answer.

Stochastically modeling multiscale stationary biological processes.

Large scale biological responses are inherently uncertain, in part as a consequence of noisy systems that do not respond deterministically to perturbations and measurement errors inherent to technological limitations. As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive (multiscale stochastic models), fail to capture the effects of noise across a system (chemical kinetic models), or fail to provide sufficient biological fidelity because of broad simplifying assumptions (stochastic differential equations). We use a new approach to modeling multiscale stationary biological processes that embraces the noise found in experimental data to provide estimates of the parameter uncertainties and the potential mis-specification of models. Our approach models the mean stationary response at each biological level given a particular expected response relationship, capturing variation around this mean using conditional Monte Carlo sampling that is statistically consistent with training data. A conditional probability distribution associated with a biological response can be reconstructed using this method for a subset of input values, which overcomes the parameter identification problem. Our approach could be applied in addition to dynamical modeling methods (see above) to predict uncertain biological responses over experimental time scales. To illustrate this point, we apply the approach to a test case in which we model the variation associated with measurements at multiple scales of organization across a reproduction-related Adverse Outcome Pathway described for teleosts.

A Multiscale Review of Behavioral Variation in Collective Foraging Behavior in Honey Bees.

The emergence of collective behavior from local interactions is a widespread phenomenon in social groups. Previous models of collective behavior have largely overlooked the impact of variation among individuals within the group on collective dynamics. Honey bees (Apis mellifera) provide an excellent model system for exploring the role of individual differences in collective behavior due to their high levels of individual variation and experimental tractability. In this review, we explore the causes and consequences of individual variation in behavior for honey bee foraging across multiple scales of organization. We summarize what is currently known about the genetic, developmental, and neurophysiological causes of individual differences in learning and memory among honey bees, as well as the consequences of this variation for collective foraging behavior and colony fitness. We conclude with suggesting promising future directions for exploration of the genetic and physiological underpinnings of individual differences in behavior in this model system.

Guest Editorial Special Issue on Neural Systems Engineering and Mathematical Modeling of Brain Dynamics Using ECoG/EEG/MEG Oscillations and Machine Learning Methods

The coherent spatiotemporal dynamical interplay among neurons, neuronal populations, cortical circuits, and networks cross the micro-, meso- and macro-scales of the human brain, providing the functional organization of the brain and supporting our daily life. The details of how these multiple scales of organization in the brain influence one another and produce a multitude of human behaviors remains a clouded mystery. Mathematical, computational, and experimental neuroscientists employ a variety of cutting-edge methods, techniques, and algorithms in both humans and animals to identify the core mechanisms that govern the perception and action characteristics of the human brain. Integration of functional MRI (fMRI)–as an indirect measure of brain activity-with electroencephalogram (EEG) and magnetoencephalogram (MEG) as direct measures of spatiotemporal and spectral fingerprints of brain dynamics has provided novel insights into how the human brain operates in the resting state and in goal-oriented task performance. The ultimate goal is to identify the fundamental mechanisms that explain brain function across multiple spatiotemporal dynamics. Although our knowledge has grown exponentially in the past few decades, the integration of this knowledge to provide a conceptual framework of emergent behavior and pattern formation occurring on different levels of spatial organization remains challenging.

Towards functional biodiversity predictions: a hierarchical modelling framework from primary productivity to biomass of upper trophic levels

Current shifts in biodiversity are driven by multiple processes of environmental and landscape change. Particularly, land use/land cover (LU/LC) dynamics are among the major drivers of biodiversity loss worldwide. In this study we aim to explore the applicability of a new modelling framework to predict top predators’ responses to LU/LC changes. The framework integrates remote-sensing based predictors, statistical inference, stochastic-dynamic simulations and spatial projections in a common and interactive approach. From an ecological modelling perspective, the main innovation of our approach lies on the integration of (1) biomass of birds of prey as an upper trophic indicator of the community characteristics that emerge from the habitat quality across multiple scales of organization and (2) fine-scale biophysical attributes to add a new level of understanding about the role of local LU/LC drivers influencing those emergent biodiversity patterns. Based on species data from published atlases this approach allowed transposing species biomass to finer resolutions, overcoming the lack of detailed information for the study area. Our demonstrative case study revealed a disruptive effect of ongoing LU/LC changes in the spatio-temporal distribution of top predators’ biomass, suggesting the possibility of an emergent disturbance pattern in habitat suitability and community stability. Comparative analysis between simulations and independent field data revealed a promising model performance. Our modelling approach highlights the importance of integrating local LU/LC functional dynamics to predict key trophic’ responses, considered as pertinent ecological indicators for biodiversity management under realistic' future changing regional scenarios.

Social network community structure and the contact-mediated sharing of commensal E. coli among captive rhesus macaques (Macaca mulatta).

In group-living animals, heterogeneity in individuals’ social connections may mediate the sharing of microbial infectious agents. In this regard, the genetic relatedness of individuals’ commensal gut bacterium Escherichia coli may be ideal to assess the potential for pathogen transmission through animal social networks. Here we use microbial phylogenetics and population genetics approaches, as well as host social network reconstruction, to assess evidence for the contact-mediated sharing of E. coli among three groups of captively housed rhesus macaques (Macaca mulatta), at multiple organizational scales. For each group, behavioral data on grooming, huddling, and aggressive interactions collected for a six-week period were used to reconstruct social network communities via the Data Cloud Geometry (DCG) clustering algorithm. Further, an E. coli isolate was biochemically confirmed and genotypically fingerprinted from fecal swabs collected from each macaque. Population genetics approaches revealed that Group Membership, in comparison to intrinsic attributes like age, sex, and/or matriline membership of individuals, accounted for the highest proportion of variance in E. coli genotypic similarity. Social network approaches revealed that such sharing was evident at the community-level rather than the dyadic level. Specifically, although we found no links between dyadic E. coli similarity and social contact frequencies, similarity was significantly greater among macaques within the same social network communities compared to those across different communities. Moreover, tests for one of our study-groups confirmed that E. coli isolated from macaque rectal swabs were more genotypically similar to each other than they were to isolates from environmentally deposited feces. In summary, our results suggest that among frequently interacting, spatially constrained macaques with complex social relationships, microbial sharing via fecal-oral, social contact-mediated routes may depend on both individuals’ direct connections and on secondary network pathways that define community structure. They lend support to the hypothesis that social network communities may act as bottlenecks to contain the spread of infectious agents, thereby encouraging disease control strategies to focus on multiple organizational scales. Future directions includeincreasing microbial sampling effort per individual to better-detect dyadic transmission events, and assessments of the co-evolutionary links between sociality, infectious agent risk, and host immune function.

Generative Biophysical Modeling of Dynamical Networks in the Olfactory System.

Generative models are computational models designed to generate appropriate values for all of their embedded variables, thereby simulating the response properties of a complex system based on the coordinated interactions of a multitude of physical mechanisms. In systems neuroscience, generative models are generally biophysically based compartmental models of neurons and networks that are explicitly multiscale, being constrained by experimental data at multiple levels of organization from cellular membrane properties to large-scale network dynamics. As such, they are able to explain the origins of emergent properties in complex systems, and serve as tests of sufficiency and as quantitative instantiations of working hypotheses that may be too complex to simply intuit. Moreover, when adequately constrained, generative biophysical models are able to predict novel experimental outcomes, and consequently are powerful tools for experimental design. We here outline a general strategy for the iterative design and implementation of generative, multiscale biophysical models of neural systems. We illustrate this process using our ongoing, iteratively developing model of the mammalian olfactory bulb. Because the olfactory bulb exhibits diverse and interesting properties at multiple scales of organization, it is an attractive system in which to illustrate the value of generative modeling across scales.

Future warmer seas: increased stress and susceptibility to grazing in seedlings of a marine habitat-forming species.

Increases in seawater temperature are expected to have negative consequences for marine organisms. Beyond individual effects, species-specific differences in thermal tolerance are predicted to modify species interactions and increase the strength of top-down effects, particularly in plant-herbivore interactions. Shifts in trophic interactions will be especially important when affecting habitat-forming species such as seagrasses, as the consequences on their abundance will cascade throughout the food web. Seagrasses are a major component of coastal ecosystems offering important ecosystem services, but are threatened by multiple anthropogenic stressors, including warming. The mechanistic understanding of seagrass responses to warming at multiple scales of organization remains largely unexplored, especially in early-life stages such as seedlings. Yet, these early-life stages are critical for seagrass expansion processes and adaptation to climate change. In this study, we determined the effects of a 3 month experimental exposure to present and predicted mean summer SST of the Mediterranean Sea (25°C, 27°C, and 29°C) on the photophysiology, size, and ecology (i.e., plant-herbivore interactions) of seedlings of the seagrass Posidonia oceanica. Warming resulted in increased mortality, leaf necrosis, and respiration as well as lower carbohydrate reserves in the seed, the main storage organ in seedlings. Aboveground biomass and root growth were also limited with warming, which could hamper seedling establishment success. Furthermore, warming increased the susceptibility to consumption by grazers, likely due to lower leaf fiber content and thickness. Our results indicate that warming will negatively affect seagrass seedlings through multiple direct and indirect pathways: increased stress, reduced establishment potential, lower storage of carbohydrate reserves, and increased susceptibly to consumption. This work provides a significant step forward in understanding the major mechanisms that will drive the capacity of seagrass seedlings to adapt and survive to warming, highlighting the potential additive effects that herbivory will have on ultimately determining seedling success.

Marine Ecosystems as Complex Adaptive Systems: Emergent Patterns, Critical Transitions, and Public Goods

Complex adaptive systems provide a unified framework for explaining ecosystem phenomena. In the past 20 years, complex adaptive systems have been sharpened from an abstract concept into a series of tools that can be used to solve concrete problems. These advances have been led by the development of new techniques for coupling ecological and evolutionary dynamics, for integrating dynamics across multiple scales of organization, and for using data to infer the complex interactions among different components of ecological systems. Focusing on the development and usage of these new methods, we discuss how they have led to an improved understanding of three universal features of complex adaptive systems, emergent patterns; tipping points and critical phenomena; and cooperative behavior. We restrict our attention primarily to marine ecosystems, which provide numerous successful examples of the application of complex adaptive systems. Many of these are currently undergoing dramatic changes due to anthropogenic perturbations, and we take the opportunity to discuss how complex adaptive systems can be used to improve the management of public goods and to better preserve critical ecosystem services.

LakeMetabolizer: an R package for estimating lake metabolism from free-water oxygen using diverse statistical models

Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). These tools have been organized into an R package that contains example data, example use-cases, and function documentation. The release package version is available on the Comprehensive R Archive Network (CRAN), and the full open-source GPL-licensed code is freely available for examination and extension online. With this unified, open-source, and freely available package, we hope to improve access and facilitate the application of metabolism in studies and management of lentic ecosystems.

Ecological and evolutionary impacts of changing climatic variability.

While average temperature is likely to increase in most locations on Earth, many places will simultaneously experience higher variability in temperature, precipitation, and other climate variables. Although ecologists and evolutionary biologists widely recognize the potential impacts of changes in average climatic conditions, relatively little attention has been paid to the potential impacts of changes in climatic variability and extremes. We review the evidence on the impacts of increased climatic variability and extremes on physiological, ecological and evolutionary processes at multiple levels of biological organization, from individuals to populations and communities. Our review indicates that climatic variability can have profound influences on biological processes at multiple scales of organization. Responses to increased climatic variability and extremes are likely to be complex and cannot always be generalized, although our conceptual and methodological toolboxes allow us to make informed predictions about the likely consequences of such climatic changes. We conclude that climatic variability represents an important component of climate that deserves further attention.

A Multilevel Modelling Approach to Measuring Changing Patterns of Ethnic Composition and Segregation Among London Secondary Schools, 2001–2010

SummaryMultilevel binomial logistic regression has recently been proposed for the special case of statistically modelling changing composition and segregation of two groups of individuals over two occasions among organizational units, enabling inferences to be made about the underlying social processes which generate these patterns. A simulation method can then be used to re-express the model parameters in the metric of any desired two-group segregation index. We generalize this combined modelling and simulation approach by proposing multilevel random-coefficient multinomial logistic regression for the general case of statistically modelling multiple groups of individuals over multiple occasions and multiple organizational scales. We illustrate this combined approach with an application to modelling changing three-group white–black–Asian ethnic composition and segregation among London secondary schools and local authorities during the first decade of the 21st century.

Metabolic ecology

Ecological theory that is grounded in metabolic currencies and constraints offers the potential to link ecological outcomes to biophysical processes across multiple scales of organization. The metabolic theory of ecology (MTE) has emphasized the potential for metabolism to serve as a unified theory of ecology, while focusing primarily on the size and temperature dependence of whole-organism metabolic rates. Generalizing metabolic ecology requires extending beyond prediction and application of standardized metabolic rates to theory focused on how energy moves through ecological systems. A bibliometric and network analysis of recent metabolic ecology literature reveals a research network characterized by major clusters focused on MTE, foraging theory, bioenergetics, trophic status, and generalized patterns and predictions. This generalized research network, which we refer to as metabolic ecology, can be considered to include the scaling, temperature and stoichiometric models forming the core of MTE, as well as bioenergetic equations, foraging theory, life-history allocation models, consumer-resource equations, food web theory and energy-based macroecology models that are frequently employed in ecological literature. We conclude with six points we believe to be important to the advancement and integration of metabolic ecology, including nomination of a second fundamental equation, complementary to the first fundamental equation offered by the MTE.

Exiting, enduring and innovating: Farm household adaptation to global zoonotic disease

Bovine spongiform encephalopathy (BSE) has been found in 25 countries, costing billions of dollars in those affected economies, and has had profound social and environmental impacts at multiple scales of organization. As a global phenomenon, the impacts of BSE were mediated directly through the environment (animal and human health) but in Canada the indirect socioeconomic impacts of BSE were far more damaging, especially for farm households. Yet, very little research has been conducted on adaptation to the indirect impacts of global environmental change, such as those mediated through the market and governance. Our goal was to examine how farm households adapted to the Canadian BSE crisis in order to better understand rural adaptations to global zoonotic diseases and to agriculture-related global environmental change as a whole. We conducted our mixed methods research in 2004–2006. Data sources included 826 survey responses, 27 individual interviews and 12 group interviews with farmers and ranchers in western Canada. Factor analysis separated out responses into three general adaptation strategies: ‘innovating’ to pursue new opportunities; ‘enduring’ or adaptations that seek stability; and ‘exiting’ from beef production or agriculture altogether. Farm household and community level innovation was a crucial adaptive strategy in the absence of governmental and expert-based support. Enduring adaptations were important to farm household survival in the short term, yet “chronic enduring” can compromise long-term adaptive capacity. Farm exiting was highly problematic during the BSE crisis as these responses were largely unexpected and often left households more vulnerable. Government support at the farm level promoted stability, with little support provided for change-orientated adaptations. Effective farm adaptation will require support for all three types of adaptive strategies and ones that are both expert-based and grassroots in nature to enable farm households in their pursuit of pluriactive and multifunctional livelihood strategies.