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Understanding how habitat attributes (e.g., patch area and sizes, connectivity) control recruitment and how this is modified by processes operating at larger spatial scales is fundamental to understanding population sustainability and developing successful long‐term restoration strategies for marine foundation species—including for globally threatened reef‐forming oysters. In two experiments, we assessed the recruitment and energy reserves of oyster recruits onto remnant reefs of the oyster Saccostrea glomerata in estuaries spanning 550 km of coastline in southeastern Australia. In the first experiment, we determined whether recruitment of oysters to settlement plates in three estuaries was correlated with reef attributes within patches (distances to patch edges and surface elevation), whole‐patch attributes (shape and size of patches), and landscape attributes (connectivity). We also determined whether environmental factors (e.g., sedimentation and water temperature) explained the differences among recruitment plates. We also tested whether differences in energy reserves of recruits could explain the differences between two of the estuaries (one high‐ and one low‐sedimentation estuary). In the second experiment, across six estuaries (three with nominally high and three with nominally low sedimentation rates), we tested the hypothesis that, at the estuary scale, recruitment and survival were negatively correlated to sedimentation. Overall, total oyster recruitment varied mostly at the scale of estuaries rather than with reef attributes and was negatively correlated with sedimentation. Percentage recruit survival was, however, similar among estuaries, although energy reserves and condition of recruits were lower at a high‐ compared to a low‐sediment estuary. Within each estuary, total oyster recruitment increased with patch area and decreased with increasing tidal height. Our results showed that differences among estuaries have the largest influence on oyster recruitment and recruit health and this may be explained by environmental processes operating at the same scale. While survival was high across all estuaries, growth and reproduction of oysters on remnant reefs may be affected by sublethal effects on the health of recruits in high‐sediment estuaries. Thus, restoration programs should consider lethal and sublethal effects of whole‐estuary environmental processes when selecting sites and include environmental mitigation actions to maximize recruitment success. [ABSTRACT FROM AUTHOR]

Collaborative processes have become instruments of choice in many programs of natural resource management (NRM) internationally. Collaborative approaches to wicked NRM problems appear attractive, given the difficulty of the issues, the wide range of stakeholders, and the dispersed responsibilities for NRM. This study of Australia’s use of collaboration to tackle wicked NRM problems at regional scales examines four diverse case studies. We analyse and contrast the challenges of specific wicked problems, the historical emergence of collaborative forms, and the benefits and challenges encountered within each of four large regions. In addition to specific organizational and financial difficulties within each collaborative arrangement, we found several common challenges (and opportunities) over relatively long timeframes. These included: managing the intersection of diverse competing interests and mandates; navigating significant issues despite uncertain knowledge; maintaining focus and effort over long periods; building local and regional momentum and continuity while adjusting to policy changes at other levels of governance; recognizing opportunities for collaboration and adaptive change; and generating collaborative platforms for linking science-policy-community leadership. We reflect on what can be learned by comparing diverse attempts at the regional scale to utilize collaboration as an instrument for managing wicked socio-ecological policy problems over long timeframes. [ABSTRACT FROM AUTHOR]

Abstract: A new daily snow cover dataset was developed using Moderate resolution Imaging Spectroradiometer (MODIS) Level-1B products for the Australian alpine region over the period 2000–2010 at 500m resolution. The dataset has been evaluated during clear-sky conditions using snow detection estimates derived from Landsat Thematic Mapper (TM) data and has been compared to the MOD10_L2 snow cover products. The ability to customise the snow detection threshold is one of the benefits of developing the Melt Area Detection Index (MADI) approach for regional conditions. The dataset provides a new satellite based observational record that may be used to characterise spatial and temporal development of Australian snow cover extent and duration. The new snow cover observations provide insight into snow characteristics in this region where significant declines in snow cover extent, season duration and a shift towards earlier snow melt date are observed. Shifts towards early season melt dates are observed for snow at 1580m and above. This includes areas which are pertinent to snow recreation activities in the region. Season length declines are attributed to earlier seasonal snowmelt rather than later season onset and may be linked to observed warming trends in the area. The MODIS based approach can be applied to other regions and other sensors to assist in evaluating snow modelling efforts and improve water resource management and snow hydrology based investigations. [Copyright &y& Elsevier]

Accurate and consistent broad-scale mapping of fire severity is an important resource for fire management as well as fire-related ecological and climate change research. Remote sensing and machine learning approaches present an opportunity to enhance accuracy and efficiency of current practices. Quantitative biophysical models of photosynthetic, non-photosynthetic and bare cover fractions have not been widely applied to fire severity studies but may provide greater consistency in comparisons of different fires across the landscape compared to reflectance-based indices. We systematically tested and compared reflectance and fractional cover candidate severity indices derived from Sentinel 2 satellite imagery using a random forest (RF) machine learning framework. Assessment of predictive power (cross-validation) was undertaken to quantify the accuracy of mapping severity of new fires. The effect of environmental variables on the accuracy of the RF predicted severity classification was examined to assess the stability of the mapping across the landscape. The results indicate that fire severity can be mapped with very high accuracy using Sentinel 2 imagery and RF supervised classification. The mean accuracy was >95% for the unburnt and extreme severity class (complete crown consumption), >85% for high severity class (full crown scorch), >80% for low severity (burnt understory, unburnt canopy) and >70% for the moderate severity class (partial canopy scorch). Higher canopy cover and higher topographic complexity was associated with a higher rate of under-prediction, due to the limitations of optical sensors in viewing the burnt understorey of low severity classes under these conditions. Further research is aimed at improving classification accuracy of low and moderate severity classes and applying the RF algorithm to hazard reduction fires. • Fire severity was mapped with high accuracy with Sentinel 2 and Random Forest. • The model with both fractional cover and reflectance indices performed the best. • Fractional cover indices especially helped to delineate unburnt from burnt. • Under-prediction of low severity (omission) was associated with high canopy density. • The Sentinel 2 algorithm has potential application to Landsat for archive mapping. [ABSTRACT FROM AUTHOR]