Establishing a regional interdisciplinary resilience center: a bottom-up approach.

Both natural and manmade disasters have severely impacted the region of Southeast Texas over the past few decades, and this has negatively affected the socio-economic well-being of the region. The state of Texas has suffered 200-250 billion dollars in damages from natural and manmade disasters since 2010. Given the region's strategic importance to the nation's energy and security, developing resilience knowledge and multi-disaster resilience research focused on issues pertaining to the region is needed. This paper describes the structure and process of building a center for multi-disaster resilience at a regional public university. By utilizing a bottom-up approach, the Center's mission and design are broadly democratized through the participation of a variety of scholars and various stakeholders with whom they interact. Resilience needs specific to the Southeast Texas region are examined, as is the relationship between resilience and the academic disciplines of the stakeholders involved. The issues of resilience in the region are discussed as well as the future steps for the Center's continued growth and development for the study of resilience.

Limitations of trait-based approaches for stressor assessment: The case of freshwater invertebrates and climate drivers.

The appeal of trait-based approaches for assessing environmental vulnerabilities arises from the potential insight they provide into the mechanisms underlying the changes in populations and community structure. Traits can provide ecologically based explanations for observed responses to environmental changes, along with predictive power gained by developing relationships between traits and environmental variables. Despite these potential benefits, questions remain regarding the utility and limitations of these approaches, which we explore focusing on the following questions: (a) How reliable are predictions of biotic responses to changing conditions based on single trait-environment relationships? (b) What factors constrain detection of single trait-environment relationships, and how can they be addressed? (c) Can we use information on meta-community processes to reveal conditions when assumptions underlying trait-based studies are not met? We address these questions by reviewing published literature on aquatic invertebrate communities from stream ecosystems. Our findings help to define factors that influence the successful application of trait-based approaches in addressing the complex, multifaceted effects of changing climate conditions on hydrologic and thermal regimes in stream ecosystems. Key conclusions are that observed relationships between traits and environmental stressors are often inconsistent with predefined hypotheses derived from current trait-based thinking, particularly related to single trait-environment relationships. Factors that can influence findings of trait-based assessments include intercorrelations of among traits and among environmental variables, spatial scale, strength of biotic interactions, intensity of habitat disturbance, degree of abiotic stress, and methods of trait characterization. Several recommendations are made for practice and further study to address these concerns, including using phylogenetic relatedness to address intercorrelation. With proper consideration of these issues, trait-based assessment of organismal vulnerability to environmental changes can become a useful tool to conserve threatened populations into the future.

Vulnerability of stream community composition and function to projected thermal warming and hydrologic change across ecoregions in the western United States.

Shifts in biodiversity and ecological processes in stream ecosystems in response to rapid climate change will depend on how numerically and functionally dominant aquatic insect species respond to changes in stream temperature and hydrology. Across 253 minimally perturbed streams in eight ecoregions in the western USA, we modeled the distribution of 88 individual insect taxa in relation to existing combinations of maximum summer temperature, mean annual streamflow, and their interaction. We used a heat map approach along with downscaled general circulation model (GCM) projections of warming and streamflow change to estimate site-specific extirpation likelihood for each taxon, allowing estimation of whole-community change in streams across these ecoregions. Conservative climate change projections indicate a 30-40% loss of taxa in warmer, drier ecoregions and 10-20% loss in cooler, wetter ecoregions where taxa are relatively buffered from projected warming and hydrologic change. Differential vulnerability of taxa with key functional foraging roles in processing basal resources suggests that climate change has the potential to modify stream trophic structure and function (e.g., alter rates of detrital decomposition and algal consumption), particularly in warmer and drier ecoregions. We show that streamflow change is equally as important as warming in projected risk to stream community composition and that the relative threat posed by these two fundamental drivers varies across ecoregions according to projected gradients of temperature and hydrologic change. Results also suggest that direct human modification of streams through actions such as water abstraction is likely to further exacerbate loss of taxa and ecosystem alteration, especially in drying climates. Management actions to mitigate climate change impacts on stream ecosystems or to proactively adapt to them will require regional calibration, due to geographic variation in insect sensitivity and in exposure to projected thermal warming and hydrologic change.

A structured and dynamic framework to advance traits-based theory and prediction in ecology.

Predicting changes in community composition and ecosystem function in a rapidly changing world is a major research challenge in ecology. Traits-based approaches have elicited much recent interest, yet individual studies are not advancing a more general, predictive ecology. Significant progress will be facilitated by adopting a coherent theoretical framework comprised of three elements: an underlying trait distribution, a performance filter defining the fitness of traits in different environments, and a dynamic projection of the performance filter along some environmental gradient. This framework allows changes in the trait distribution and associated modifications to community composition or ecosystem function to be predicted across time or space. The structure and dynamics of the performance filter specify two key criteria by which we judge appropriate quantitative methods for testing traits-based hypotheses. Bayesian multilevel models, dynamical systems models and hybrid approaches meet both these criteria and have the potential to meaningfully advance traits-based ecology.