Adaptation is now firmly embedded in the societal discourse regarding the management of climate risk. In this discourse, adaptation planning and implementation at the local level is seen as particularly important for developing robust responses to climate change. However, it is not clear whether the mantra that adaptation is local holds true given the multi-level nature of climate risk governance.Using a multi-method approach, this paper examines the extent to which adaptation should be framed as a local issue and, specifically, the role of local government in adaptation relative to other actors. In so doing, the paper first explores the extent to which the local framing of adaptation is embedded in the international adaptation literature. This is followed by a specific case study from Southeast Queensland, Australia, which focuses on the critical examination of the processes of responsibility shifting and taking among actors involved in coastal adaptation planning. Results indicate the assumption that adaptation is local remains widely held in adaptation science, although counter arguments can be readily identified. Interviews with adaptation actors revealed unclear divisions of responsibility for climate change adaptation as a significant constraint on actors' willingness to implement adaptation. Furthermore, attributing responsibility for adaptation to local actors might not necessarily be a robust strategy, due to the existence of particularly strong constraints and value conflicts at local levels of governance. Greater appreciation by researchers and practitioners for the interactions between local actors and those at higher levels of governance in shaping response capacity may contribute to more equitable and effective allocations of responsibilities for adaptation action.
The adaptation science enterprise has expanded rapidly in recent years, presumably in response to growth in demand for knowledge that can facilitate adaptation policy and practice. However, evidence suggests such investments in adaptation science have not necessarily translated into adaptation implementation. One potential constraint on adaptation may be the underlying heuristics that are used as the foundation for both adaptation research and practice. Here, we explore the adaptation academic literature with the objective of identifying adaptation heuristics, assessing the extent to which they have become entrenched within the adaptation discourse, and discussing potential weaknesses in their framing that could undermine adaptation efforts. This investigation is supported by a multi-method analysis that includes both a quantitative content analysis of the adaptation literature that evidences the use of adaptation heuristics and a qualitative analysis of the implications of such heuristics for enhancing or hindering the implementation of adaptation. Results demonstrate that a number of heuristic devices are commonly used in both the peer-reviewed adaptation literature as well as within grey literature designed to inform adaptation practitioners. Furthermore, the apparent lack of critical reflection upon the robustness of these heuristics for diverse contexts may contribute to potential cognitive bias with respect to the framing of adaptation by both researchers and practitioners. We discuss this phenomenon by drawing upon heuristic-analytic theory, which has explanatory utility in understanding both the origins of such heuristics as well as the measures that can be pursued toward the cogeneration of more robust approaches to adaptation problem-solving.
This study presents an automated methodology to generate training data for surface water mapping from a single Sentinel-2 granule at 10 m (4 band, VIS/NIR) or 20 m (9 band, VIS/NIR/SWIR) resolution without the need for ancillary training data layers. The 20 m method incorporates an ensemble of three spectral indexes with optimal band thresholds, whereas the 10 m method achieves similar results using fewer bands and a single spectral index. A spectrally balanced and randomly generated set of training data based on the index values and optimal thresholds is used to fit machine learning classifiers. Statistical validation compares the 20 m ensemble-only method to the 20 m ensemble method with a random forest classifier. Results show the 20 m ensemble-only method had an overall accuracy of 89.5% (±1.7%), whereas the ensemble method combined with the random forest classifier performed better, with a ~4.8% higher overall accuracy: 20 m method (94.3% (±1.3%)) with optimal spectral index and SWIR thresholds of −0.03 and 800, respectively, and 10 m method (93.4% (±1.5%)) with optimal spectral index and NIR thresholds of −0.01 and 800, respectively. Comparison of other supervised classifiers trained automatically with the framework typically resulted in less than 1% accuracy improvement compared with the random forest, suggesting that training data quality is more important than classifier type. This straightforward framework enables accurate surface water classification across diverse geographies, making it ideal for development into a decision support tool for water resource managers.
A small number of species, including Mimosa pudica, use rapid leaf movement as a presumptive defensive strategy. How movement-based defenses change in response to mechanical damage and whether changes are localized or systemic is unknown. This is in contrast to a substantial literature describing how mechanical leaf damage can cause morphological and chemical responses within a diversity of plant species. Depending on the species and the stimuli, these chemical and morphological responses can be localized to the tissues damaged or systemic throughout the plant body. Here we report the results of a small experiment designed to test the following: (i) whether mechanical leaf damage influences subsequent leaf closure behavior, and (ii) whether changes were systemic or localized. To do this, we scored leaves using a behavioral assay (time-to-reopen leaves following a subsequent touch stimuli) for several days before and following mechanical damage. Leaves above and below the damaged leaf were observed, on damaged and undamaged plants, allowing us to assess whether any change was systemic. We found leaf damage caused strong localized effects, greatly increasing the time-to-reopen of the damaged, but not adjacent, leaves. Neither the physiological cause nor fitness consequences of this behavioral shift are known. Interestingly, this altered behavior resulted in damaged leaves remaining “hidden” longer than undamaged leaves. If leaf closure reduces risk of herbivory, there could be adaptive value, analogous to inducible chemical and morphological defenses.
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